Question: I am looking for papers on accelerating Large Language Model (LLM) inference speeds. I am specifically interested in algorithmic approaches like Speculative Decoding or Blockwise Parallel Decoding. However, please exclude any papers that primarily achieve acceleration through model compression techniques such as Weight Quantization (e.g., INT8, FP4) or Network Pruning.

Answer: SwiftSpec: Ultra-Low Latency LLM Decoding by Scaling Asynchronous Speculative Decoding

Answer: SpecPipe: Accelerating Pipeline Parallelism-based LLM Inference with Speculative Decoding

Answer: AdaDecode: Accelerating LLM Decoding with Adaptive Layer Parallelism

Answer: MagicDec: Breaking the Latency-Throughput Tradeoff for Long Context Generation with Speculative Decoding

Answer: TriForce: Lossless Acceleration of Long Sequence Generation with Hierarchical Speculative Decoding

Answer: Fast Inference from Transformers via Speculative Decoding

Answer: Accelerating Large Language Model Decoding with Speculative Sampling

Answer: EAGLE: Speculative Sampling Requires Rethinking Feature Uncertainty

Answer: EAGLE-2: Faster Inference of Language Models with Dynamic Draft Trees

Answer: Sequoia: Scalable, Robust, and Hardware-aware Speculative Decoding

Answer: SpecInfer: Accelerating Generative Large Language Model Serving with Tree-based Speculative Inference and Verification

Answer: Interpretable Contrastive Monte Carlo Tree Search Reasoning

Answer: Blockwise Parallel Decoding for Deep Autoregressive Models

Answer: Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads

Answer: OPT-Tree: Speculative Decoding with Adaptive Draft Tree Structure

Answer: DySpec: Faster Speculative Decoding with Dynamic Token Tree Structure

Answer: Dynamic Depth Decoding: Faster Speculative Decoding for LLMs

Answer: Graph-Structured Speculative Decoding

Answer: S2D: Sorted Speculative Decoding For More Efficient Deployment of Nested Large Language Models

Answer: Decoding Speculative Decoding

Answer: Speculative Decoding via Early-exiting for Faster LLM Inference with Thompson Sampling Control Mechanism

Answer: Draft & Verify: Lossless Large Language Model Acceleration via Self-Speculative Decoding

Answer: SWIFT: On-the-Fly Self-Speculative Decoding for LLM Inference Acceleration

Answer: DEL: Context-Aware Dynamic Exit Layer for Efficient Self-Speculative Decoding

Answer: Pipeline Parallelism is All You Need for Optimized Early-Exit Based Self-Speculative Decoding

Answer: Amphista: Bi-directional Multi-head Decoding for Accelerating LLM Inference

Answer: Lossless Acceleration of Large Language Model via Adaptive N-gram Parallel Decoding

Answer: REST: Retrieval-Based Speculative Decoding

Answer: Turning Trash into Treasure: Accelerating Inference of Large Language Models with Token Recycling

Answer: CREST: Effectively Compacting a Datastore For Retrieval-Based Speculative Decoding

Answer: DReSD: Dense Retrieval for Speculative Decoding

Answer: RASD: Retrieval-Augmented Speculative Decoding

Answer: SAM Decoding: Speculative Decoding via Suffix Automaton

Answer: Lossless Acceleration of Large Language Models with Hierarchical Drafting based on Temporal Locality in Speculative Decoding

Answer: Accelerating LLM Inference with Lossless Speculative Decoding Algorithms for Heterogeneous Vocabularies

Answer: EAGLE-3: Scaling up Inference Acceleration of Large Language Models via Training-Time Test

Answer: Speculative Decoding for Multi-Sample Inference

Answer: Judge Decoding: Faster Speculative Sampling Requires Going Beyond Model Alignment

Answer: S^4C: Speculative Sampling with Syntactic and Semantic Coherence for Efficient Inference of Large Language Models

Answer: Dynamic-Width Speculative Beam Decoding for Efficient LLM Inference

Answer: Hierarchical Verification of Speculative Beams for Accelerating LLM Inference

Answer: SpecExtend: A Drop-in Enhancement for Speculative Decoding of Long Sequences

Answer: Hardware-Aware Parallel Prompt Decoding for Memory-Efficient Acceleration of LLM Inference

Answer: FR-Spec: Accelerating Large-Vocabulary Language Models via Frequency-Ranked Speculative Sampling

Answer: VOCABTRIM: Vocabulary Pruning for Efficient Speculative Decoding in LLMs

Answer: DynaSpec: Context-aware Dynamic Speculative Sampling for Large-Vocabulary Language Models

Answer: Acceleration Multiple Heads Decoding for LLM via Dynamic Tree Attention

Answer: FastMTP: Accelerating LLM Inference with Enhanced Multi-Token Prediction

Answer: Accelerating Transformer Inference for Translation via Parallel Decoding

Answer: Falcon: Faster and Parallel Inference of Large Language Models through Enhanced Semi-Autoregressive Drafting and Custom-Designed Decoding Tree

Answer: Parallel Speculative Decoding with Adaptive Draft Length

Answer: SpecTr: Fast Speculative Decoding via Optimal Transport

Answer: Block Verification Accelerates Speculative Decoding

Answer: Exploring and Improving Drafts in Blockwise Parallel Decoding

Answer: Online Speculative Decoding

Answer: DistillSpec: Improving Speculative Decoding via Knowledge Distillation

Answer: SpecDec++: Boosting Speculative Decoding via Adaptive Candidate Lengths

Answer: EMS-SD: Efficient Multi-sample Speculative Decoding for Accelerating Large Language Models

Answer: Speculative Streaming: Fast LLM Inference without Auxiliary Models

Answer: Recurrent Drafter for Fast Speculative Decoding in Large Language Models

Answer: Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decoding

Answer: Hydra: Sequentially-Dependent Draft Heads for Medusa Decoding

Answer: Accelerated Test-Time Scaling with Model-Free Speculative Sampling

Answer: BASS: Batched Attention-optimized Speculative Sampling

Answer: GliDe with a CaPE: A Low-Hassle Method to Accelerate Speculative Decoding

Answer: Speculative Diffusion Decoding: Accelerating Language Generation through Diffusion

Answer: BanditSpec: Adaptive Speculative Decoding via Bandit Algorithms

Answer: ParallelSpec: Parallel Drafter for Efficient Speculative Decoding

Answer: LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding

Artificial Intelligence and Computing


Question: Please help me find papers on food trade or changes in carbon emissions due to the Russia-Ukraine conflict, including MRIO-based analysis of trade and simulations of land use change or future multi-scenarios.

Answer: The Russia-Ukraine war decreases food affordability but could reduce global greenhouse gas emissions

Answer: Trade scenarios compensating for halted wheat and maize exports from Russia and Ukraine increase carbon emissions without easing food insecurity

Answer: Telecoupled impacts of the Russia–Ukraine war on global cropland expansion and biodiversity

Answer: Adoption of plant-based diets across Europe can improve food resilience against the Russia–Ukraine conflict

Answer: Global environmental impacts of food system from regional shock: Russia-Ukraine war as an example

Answer: War's cascading global effects

Answer: Cascading socio-economic and financial impacts of the Russia-Ukraine war differ across sectors and regions

Answer: High energy and fertilizer prices are more damaging than food export curtailment from Ukraine and Russia for food prices, health and the environment

Answer: The impact of Russia-Ukraine conflict on global food security

Answer: Global food-miles account for nearly 20% of total food-systems emissions

Answer: Disrupted harvests: how Ukraine – Russia war influences global food systems – a systematic review

Engineering and Technology


Question: I am using a large language model to build an environmental extended input-output model calculation script, but different environmental elements such as implied carbon footprint, virtual water footprint, etc. will require different input data. Can you recommend some papers that cover input-output models of multiple environmental elements?

Answer: Integrating ecological and water footprint accounting in a multi-regional input–output framework

Answer: Nexus between water-energy-carbon footprint network: Multiregional input-output and coupling coordination degree analysis

Answer: Tele-connections, driving forces and scenario simulation of agricultural land, water use and carbon emissions in China's trade

Answer: Identifying the key sectors for regional energy, water and carbon footprints from production-, consumption- and network-based perspectives

Answer: Carbon, water and land use accounting: Consumption vs production perspectives

Answer: Multi-regional input-output and linkage analysis for water-PM2.5 nexus

Answer: A highly resolved MRIO database for analyzing environmental footprints and Green Economy Progress

Answer: An extended environmental input–output lifecycle assessment model to study the urban food–energy–water nexus

Answer: Rethinking environmental stress from the perspective of an integrated environmental footprint: Application in the Beijing industry sector

Answer: Water, energy, and carbon dioxide footprints of the construction sector: A case study on developed and developing economies

Answer: Mapping urban energy–water–land nexus within a multiscale economy: A case study of four megacities in China

Answer: Water-Energy-Carbon Emissions nexus analysis of China: An environmental input-output model-based approach

Answer: EXIOBASE 3: Developing a Time Series of Detailed Environmentally Extended Multi-Regional Input-Output Tables

Answer: Assessing the environmental impacts of EU consumption at macro-scale

Answer: Companies on the Scale

Answer: Understanding the trends in Denmark's global food trade-related greenhouse gas and resource footprint

Answer: USEEIO: A new and transparent United States environmentally-extended input-output model

Answer: USEEIO v2.0, The US Environmentally-Extended Input-Output Model v2.0

Answer: Analysis of complementary methodologies to assess the environmental impact of Luxembourg's net consumption

Answer: Sustainability assessment of U.S. final consumption and investments: triple-bottom-line input–output analysis

Answer: Carbon and water footprint accounts of Italy: A Multi-Region Input-Output approach

Answer: Product Environmental Life-Cycle Assessment Using Input-Output Techniques

Answer: The need to decelerate fast fashion in a hot climate - A global sustainability perspective on the garment industry

Answer: Carbon and Materials Embodied in the International Trade of Emerging Economies

Engineering and Technology


Question: Please find papers on training-free model merging that focus on resolving knowledge conflicts in task vectors, adopt parameter-specific trimming strategies (including projection for linear weights and masking for normalization scalers/shifts), and report experimental results on ViT-B/32, ViT-L/14 (vision tasks), RoBERTa (NLP tasks), and BLIP (vision-language tasks).

Answer: CAT Merging: A Training-Free Approach for Resolving Conflicts in Model Merging

Artificial Intelligence and Computing


Question: Please help me locate papers on model merging that minimize feature drift via layer-wise task vector fusion, formulate merging as a convex quadratic optimization problem with closed-form solutions for linear/normalization layer parameters, and demonstrate performance improvements on vision (ViT-B/32, ViT-L/14) and vision-language (BLIP) benchmarks.

Answer: Towards Minimizing Feature Drift in Model Merging: Layer-wise Task Vector Fusion for Adaptive Knowledge Integration

Artificial Intelligence and Computing


Question: I need research papers that develop ductile fracture models specifically for magnesium alloys under negative stress triaxiality (η<0) compression-shear loading conditions, where the models must incorporate the tension-compression asymmetry characteristic of HCP materials and include experimental validation from actual compression-shear test scenarios.

Answer: Ductile Fracture Prediction in Mg-ZM51M Alloy Using Inverse-Calibrated Damage Models

Answer: Modeling asymmetric fracture mechanics of Mg alloy wire in drawing process

Answer: Stress-state dependency of ductile fracture in an extruded magnesium alloy and its underlying mechanisms

Answer: Two-component DF2016 criterion to characterize the fracture behavior of magnesium rare-earth alloys

Engineering and Technology


Question: Please retrieve research papers on the influence of process parameters on joint performance in Solid Self-Piercing Riveting (SSPR) that incorporate both experimental validation and finite element simulation analysis, with the constraint that no discussion of hollow-rivet Self-Piercing Riveting (SPR), hot riveting, or electromagnetic riveting technologies is permitted outside the introduction section.

Answer: The effect of material properties and joining process parameters on behavior of self-pierce riveting joints made with the solid rivet

Answer: The failure mechanics analysis of the solid self-piercing riveting joints

Answer: Design of aluminium solid self-piercing rivets for joining aluminium sheets by material and geometric modification

Answer: Numerical Simulation and Experiment of Self-Piercing Riveting with Solid Rivet Joining Multi-Layer Aluminum Sheets

Answer: Self-locking self-pierce riveting: a new self-pierce riveting technology for multi-material applications in lightweight car body structures

Answer: Development of a rivet geometry for solid self-piercing riveting of thermally loaded CFRP-metal joints in automotive construction

Answer: Mechanical joining of glass fibre reinforced polymer (GFRP) through an innovative solid self-piercing rivet

Answer: Adaptation of a solid self-piercing rivet made of aluminum using numerical simulation to extend the application limits

Answer: The numerical analysis of the effect of the joining process parameters on self-piercing riveting using the solid rivet

Engineering and Technology


Question: Please help find peer-reviewed papers published on or before November 1, 2025, which propose Global Flexible Power Point Tracking (GFPPT)/global flexible power control technologies suitable for photovoltaic (PV) arrays under partial shading conditions (under such conditions, PV arrays exhibit a multi-peak power-voltage (P-V) curve due to the presence of bypass diodes). The target papers need only meet one of the following two core requirements: 1. Be able to achieve the global maximum available power (GMPP or P_av) of the PV array under partial shading conditions without being trapped at local power peaks; 2. Be able to regulate the PV array to track a user-defined curtailed active power reference (i.e., Flexible Power Point Tracking, FPPT), namely to meet the operation requirement of P_ref < P_av, including the constant power generation operation mode. Please only select papers that meet all of the following criteria: 1. Clearly elaborate on the control architecture and specific implementation scheme, including the generation method of the Flexible Power Point Tracking (FPPT) reference value and how the converter/inverter control enforces the PV-side operating point to track this reference value; 2. Conduct comparisons with at least one baseline method (such as conventional Maximum Power Point Tracking (MPPT), scan-based Global Maximum Power Point Tracking (GMPP)/Flexible Power Point Tracking (FPPT), etc.) and provide quantitative performance metrics (e.g., tracking error, settling time, and/or energy yield efficiency, etc.).

Answer: Multiple-to-single maximum power point tracking for empowering conventional MPPT algorithms under partial shading conditions

Answer: Design and Analysis of an Adaptive Global Maximum Power Point Tracking Algorithm for Enhanced Partial Shading Detection and GMPP Tracking

Answer: A High-Performance Global Maximum Power Point Tracker of PV System for Rapidly Changing Partial Shading Conditions

Answer: Global Flexible Power Point Tracking in Photovoltaic Systems Under Partial Shading Conditions

Answer: Improved Secant-Based Global Flexible Power Point Tracking in Photovoltaic Systems Under Partial Shading Conditions

Answer: Global Flexible Power Control of Photovoltaic Systems Under Partial Shading Conditions Based on Direct Multi-Peak P-V Curve-Fitting

Answer: Fast global flexible power point tracking of photovoltaic systems under partial shading condition with tracking-affiliated environmental parameter estimation

Answer: An enhanced scanning technique for flexible power point tracking under partial shading condition

Answer: Model-based flexible power point tracking method for photovoltaic systems under partial shading conditions

Answer: Dynamic Reserve Power Point Tracking in Grid-Connected Photovoltaic Power Plants

Answer: Power control strategy of PV system for active power reserve under partial shading conditions

Answer: A flexible power point tracking algorithm for photovoltaic system under partial shading condition

Answer: High Voltage Selective Global Flexible Power Point Tracking for Photovoltaic Systems

Answer: Enhancing PV system grid stability through reliable flexible power point tracking under partial shading

Answer: Universal Power Point Tracking Using Binary Search: A Multi-Scenario Approach for Photovoltaic Systems

Answer: A novel global FPPT strategy for PV system under partial shading condition

Answer: Comparative Study of Genetic Algorithms and Particle Swarm Optimization for Flexible Power Point Tracking in Photovoltaic Systems under Partial Shading

Answer: Advanced Single-Phase Non-Isolated Microinverter with Time-Sharing Maximum Power Point Tracking Control Strategy

Answer: Visual Re-Initialization Model Development Methodology for Solving Problems Regarding Metaheuristic Algorithm-Based MPPT Applications

Answer: An adapted model predictive control MPPT for validation of optimum GMPP tracking under partial shading conditions

Answer: FPPT and Fixed-Time Sliding Mode Controller for Single-Stage Grid-Integrated PV Systems

Answer: Multi-step depth model predictive control for photovoltaic maximum power point tracking under partial shading conditions

Answer: Application of a Chaotic Quantum Bee Colony and Support Vector Regression to Multipeak Maximum Power Point Tracking Control Method Under Partial Shading Conditions

Answer: Archimedes optimization algorithm (AOA)-Based global maximum power point tracking for a photovoltaic system under partial and complex shading conditions

Answer: External Current Source–Based Unilluminated PV Partial Shading Emulation System Verified Through the Hybrid Global Search Adaptive Perturb and Observe MPPT Algorithm

Answer: Simulation and experimental validation of fast adaptive particle swarm optimization strategy for photovoltaic global peak tracker under dynamic partial shading

Answer: Revolutionizing Photovoltaic Systems: An Innovative Approach to Maximum Power Point Tracking Using Enhanced Dandelion Optimizer in Partial Shading Conditions

Answer: A comprehensive comparison of advanced metaheuristic photovoltaic maximum power tracking algorithms during dynamic and static environmental conditions

Answer: A Novel Hybrid MPPT Technique to Maximize Power Harvesting from PV System under Partial and Complex Partial Shading

Answer: Enhancing MPPT Performance in Partially Shaded PV Systems under Sensor Malfunctioning with Fuzzy Control

Answer: A novel meta-heuristic optimization algorithm based MPPT control technique for PV systems under complex partial shading condition

Answer: Enhanced ANN-Based MPPT for Photovoltaic Systems: Integrating Metaheuristic and Analytical Algorithms for Optimal Performance Under Partial Shading

Answer: A direct control based maximum power point tracking method for photovoltaic system under partial shading conditions using particle swarm optimization algorithm

Answer: A novel hybrid GWO–PSO-based maximum power point tracking for photovoltaic systems operating under partial shading conditions

Answer: Designing a Generic Multi-Modality Processing Adapter for the Practical Implementation of the Photovoltaic Regulated Power Point Tracking under the Partial Shading

Answer: Coordinated Power Management and Control of Standalone PV-Hybrid System With Modified IWO-Based MPPT

Answer: New perturb and observe algorithm based on trapezoidal rule: Uniform and partial shading conditions

Answer: Increased Energy Harvesting from shaded PV power plant using a fast converging Fruit fly Algorithm

Answer: A new neuro-fuzzy controller based maximum power point tracking for a partially shaded grid-connected photovoltaic system

Engineering and Technology


Question: I am designing a grid-connected PV system that must be equipped with flexible active power control capability (global maximum power point tracking/constant power generation) to provide grid-support services. Under partial shading conditions, the power-voltage (P–V) curve of the PV array exhibits a multi-peak characteristic, so traditional Flexible Power Point Tracking (FPPT) schemes designed for uniform irradiance conditions may be trapped at a local peak and cannot reliably achieve global maximum power point tracking/constant power generation. Please help find peer-reviewed papers published on or before November 1, 2025, which propose novel Flexible Power Point Tracking (FPPT) schemes to improve the active power control capability of PV systems under partial shading conditions.

Answer: Multiple-to-single maximum power point tracking for empowering conventional MPPT algorithms under partial shading conditions

Answer: Design and Analysis of an Adaptive Global Maximum Power Point Tracking Algorithm for Enhanced Partial Shading Detection and GMPP Tracking

Answer: A High-Performance Global Maximum Power Point Tracker of PV System for Rapidly Changing Partial Shading Conditions

Answer: Enhancing the tracking techniques for the global maximum power point under partial shading conditions

Answer: Global Flexible Power Point Tracking in Photovoltaic Systems Under Partial Shading Conditions

Answer: Global maximum power point tracking based on ANFIS approach for PV array configurations under partial shading conditions

Answer: Improved Secant-Based Global Flexible Power Point Tracking in Photovoltaic Systems Under Partial Shading Conditions

Answer: Novel Fast-Speed Partial-Shading-Tolerant Flexible Power Point Tracking for Photovoltaic Systems With Explicit Key Points Estimation

Answer: Fast global flexible power point tracking of photovoltaic systems under partial shading condition with tracking-affiliated environmental parameter estimation

Answer: An enhanced scanning technique for flexible power point tracking under partial shading condition

Answer: Target Voltage Iterations Based Global Flexible Power Point Tracking Algorithm Under Partial Shading Photovoltaic Systems

Answer: Model-based flexible power point tracking method for photovoltaic systems under partial shading conditions

Answer: Dynamic Reserve Power Point Tracking in Grid-Connected Photovoltaic Power Plants

Answer: A novel global FPPT strategy for PV system under partial shading condition

Answer: Power control strategy of PV system for active power reserve under partial shading conditions

Answer: A flexible power point tracking algorithm for photovoltaic system under partial shading condition

Answer: Flexible Power Point Tracking for Photovoltaic Systems under Partial Shading Conditions

Answer: Enhancing PV system grid stability through reliable flexible power point tracking under partial shading

Answer: A genetic algorithm approach for flexible power point tracking in partial shading conditions

Answer: Global Flexible Power Point Tracking Based on Reinforcement Learning for Partially Shaded PV Arrays

Answer: Dynamic Global Maximum Power Point Tracking for Partially Shaded PV Arrays in Grid-Connected PV Systems

Answer: Robust Maximum Power Point Tracking in PV Generation System: A Hybrid ANN-Backstepping Approach With PSO-GA Optimization

Answer: Global MPPT Based on Machine-Learning for PV Arrays Operating under Partial Shading Conditions

Answer: Optimized Global Maximum Power Point Tracking of Photovoltaic Systems Based on Rectangular Power Comparison

Answer: Metaheuristics Assisted Efficiency Maximizing Flexible Power Point Tracking of a Photovoltaic Array Under the Partial Shading

Answer: An effective maximum power point tracking technique for partially shaded photovoltaic system under rapidly changing atmospheric conditions

Answer: Global maximum power point tracking for photovoltaic systems under partial and complex shading conditions using a PID based search algorithm (PSA)

Answer: An optimal adaptive neuro-fuzzy inference system for photovoltaic power system optimization under partial shading conditions

Answer: Priority queue-based switching matrix algorithm for adaptive neuro-fuzzy inference system assisted MPPT controlled PV system

Answer: Archimedes optimization algorithm (AOA)-Based global maximum power point tracking for a photovoltaic system under partial and complex shading conditions

Answer: Comprehensive Analysis of Improved Hunter–Prey Algorithms in MPPT for Photovoltaic Systems Under Complex Localized Shading Conditions

Answer: Experimental investigation of ANFIS-PSO MPPT control with enriched voltage gain DC–DC converter for grid-tied PV applications

Answer: Simulation and experimental validation of fast adaptive particle swarm optimization strategy for photovoltaic global peak tracker under dynamic partial shading

Answer: Improved Photovoltaic MPPT Algorithm Based on Ant Colony Optimization and Fuzzy Logic Under Conditions of Partial Shading

Answer: FPPT and Fixed-Time Sliding Mode Controller for Single-Stage Grid-Integrated PV Systems

Answer: Harris hawk optimization-based MPPT control for PV systems under partial shading conditions

Answer: A Particle Swarm Optimization–Adaptive Weighted Delay Velocity-Based Fast-Converging Maximum Power Point Tracking Algorithm for Solar PV Generation System

Answer: Novel Hybrid Mexican Axolotl Optimization with Fuzzy Logic for Maximum Power Point Tracker of Partially Shaded Photovoltaic Systems

Answer: AI-Enhanced MPPT Control for Grid-Connected Photovoltaic Systems Using ANFIS-PSO Optimization

Answer: Framework of Maximum Solar Energy Conversion Using Lyapunov‐Based Model Reference Adaptive Control Technique in Stochastic Weather Conditions

Answer: Enhanced Maximum Power Point Tracking Using Modified PSO Hybrid With MPC Under Partial Shading Conditions

Engineering and Technology


Question: Provide papers on Time-Domain Thermoreflectance (TDTR) experimental design optimization published before November 2025. I am looking for studies that discuss physical parameter selection such as spot size or transducer thickness based on sensitivity analysis. Exclude papers that solely introduce data fitting software or GUI toolkits without discussing the underlying physical design optimization.

Answer: Time-domain thermoreflectance (TDTR) measurements of anisotropic thermal conductivity using a variable spot size approach

Answer: A new elliptical-beam method based on time-domain thermoreflectance (TDTR) to measure the in-plane anisotropic thermal conductivity and its comparison with the beam-offset method

Answer: Accurate measurements of cross-plane thermal conductivity of thin films by dual-frequency time-domain thermoreflectance (TDTR)

Answer: Time-domain thermoreflectance technique using multiple delayed probe pulses for high-throughput data acquisition and analysis

Answer: Simultaneous measurement of thermal conductivity, heat capacity, and interfacial thermal conductance by leveraging negative delay-time data in time-domain thermoreflectance

Answer: Thermal Boundary Resistance in GaN Films Measured by Time Domain Thermoreflectance with Robust Monte Carlo Uncertainty Estimation

Answer: Anisotropic thermal conductivity measurement using a new Asymmetric-Beam Time-Domain Thermoreflectance (AB-TDTR) method

Answer: An accurate method to determine nano-film thickness in diamond anvil cells for time domain thermoreflectance measurements

Engineering and Technology


Question: Please provide papers published in Applied Energy that study both district cooling and safe reinforcement learning. The papers should be released before the November of 2025.

Answer: Reliable Non-Parametric Techniques for Energy System Operation and Control: Fundamentals and Applications of Constraint Learning and Safe Reinforcement Learning Methods

Answer: District cooling system control for providing regulation services based on safe reinforcement learning with barrier functions

Answer: Voltage control of distribution grid with district cooling systems based on scenario-classified reinforcement learning

Engineering and Technology


Question: Please find papers published before 1 Nov 2025 that use self-supervised learning (SSL) on longitudinal retinal OCT data (multiple visits per eye) to predict age-related macular degeneration progression within an explicit future time window. The SSL method must exploit temporal information during pretraining and the paper must report quantitative results on an AMD progression prediction downstream task.

Answer: Morph-SSL: self-supervision with longitudinal morphing for forecasting AMD progression from OCT volumes

Answer: TINC: Temporally Informed Non-Contrastive Learning for Disease Progression Modeling in Retinal OCT Volumes

Answer: Modeling Disease Progression in Retinal OCTs with Longitudinal Self-Supervised Learning

Answer: 3DTINC: Time-Equivariant Non-Contrastive Learning for Predicting Disease Progression from Longitudinal OCTs

Answer: Morph-SSL: Self-Supervision with Longitudinal Morphing to Predict AMD Progression from OCT

Answer: Learning Temporally Equivariance for Degenerative Disease Progression in OCT by Predicting Future Representations

Medicine and Life Sciences


Question: Could you help find papers (published before 1 Nov 2025) on federated learning for diabetic retinopathy detection or severity grading from color fundus photographs, where training is performed across at least three distinct data silos, raw images are not centralized, and the paper compares the federated model against a centralized-training baseline. Please exclude works that only simulate clients by randomly splitting a single dataset without any center or dataset distinction.

Answer: Federated Transfer Learning For Diabetic Retinopathy Detection Using CNN Architectures

Answer: Federated Learning for Diabetic Retinopathy Detection in a Multi-center Fundus Screening Network

Answer: Federated Learning for Diabetic Retinopathy Detection Using Vision Transformers

Answer: Federated Learning for Diabetic Retinopathy Diagnosis: Enhancing Accuracy and Generalizability in Under-Resourced Regions

Answer: FedDL: personalized federated deep learning for enhanced detection and classification of diabetic retinopathy

Medicine and Life Sciences


Question: Please help me find research articles on the role of integrative Chinese and Western medicine in neurodegenerative diseases and aging that include clinical trials from the past decade and scientific discoveries in delaying the aging process. The studies should focus on Traditional Chinese Medicine (TCM) interventions (such as herbal formulations like Liuwei Dihuang decoction, acupuncture, and mind-body therapies), Western pharmacological approaches (including mitochondrial antioxidants like ALCAR+LA, HSP70 modulators, and conventional neuroprotective agents), and combined therapeutic strategies. The research should cover age-related cognitive decline, Alzheimer's disease, Parkinson's disease, and other neurodegenerative conditions, with emphasis on mechanisms involving mitochondrial function, oxidative stress reduction, neurogenesis, and the microbiota-gut-brain axis。

Answer: Hekun decoction versus Femoston for women with amnestic mild cognitive impairment in early menopause: a randomized, three-arm, double-blind clinical trial

Answer: Comparison of Donepezil, Memantine, Melatonin, and Liuwei Dihuang Decoction on Behavioral and Immune Endocrine Responses of Aged Senescence-Accelerated Mouse Resistant 1 Mice

Answer: Kaixin San Jiawei granule improves cognitive function and alleviates neuronal damage in Alzheimer’s disease via multi-component and multi-target mechanisms

Answer: Liuwei Dihuang Decoction Alleviates Cognitive Dysfunction in Mice With D-Galactose-Induced Aging by Regulating Lipid Metabolism and Oxidative Stress via the Microbiota-Gut-Brain Axis

Answer: Dihuang-Yinzi Alleviates Cognition Deficits via Targeting Energy-Related Metabolism in an Alzheimer Mouse Model as Demonstrated by Integration of Metabolomics and Network Pharmacology

Answer: Randomized controlled trial of Bushen Yinao pill combined with conventional therapy on intestinal flora and cognitive function in older patients with Alzheimer's disease

Answer: Dengzhan shengmai capsule combined with donepezil hydrochloride in the treatment of Alzheimer's disease: preliminary findings, randomized and controlled clinical trial

Answer: Efficacy evaluation and metabolomics analysis of Huanglian Jiedu decoction in combination with donepezil for Alzheimer's disease treatment

Answer: Modulation of Brain Activity and Functional Connectivity by Acupuncture Combined With Donepezil on Mild-to-Moderate Alzheimer's Disease: A Neuroimaging Pilot Study

Answer: Combined therapy of Di-Huang-Yi-Zhi with Donepezil in patients with Parkinson's disease dementia

Answer: Pingchan Granule for Motor Symptoms and Non-Motor Symptoms of Parkinson's Disease: A Randomized, Double-Blind, Placebo-Controlled Study

Answer: Zishenpingchan granules for the treatment of Parkinson's disease: a randomized, double-blind, placebo-controlled clinical trial

Answer: Efficacy and safety of Tianqi Pingchan Granule, a compound Chinese herbal medicine, for levodopa-induced dyskinesia in Parkinson's disease: A randomized double-blind placebo-controlled trial

Answer: Multi-dimensional analysis on Parkinson's disease questionnaire-39 in Parkinson's patients treated with Bushen Huoxue Granule: A multicenter, randomized, double-blinded and placebo controlled trial

Answer: Zishen pingchan granules combined with pramipexole in the improvement of depressive symptoms in Parkinson's disease: a prospective, multicenter, randomized, double-blind, controlled clinical study

Medicine and Life Sciences


Question: Could you help check whether there are any papers that investigate integrative Chinese and Western medicine not for general cardiac diseases but rather specifically for heart failure that include clinical trials from the past decade and scientific discoveries in myocardial vitality restoration? These studies should focus on Traditional Chinese Medicine interventions such as herbal formulations (e.g., Qili Qiangxin, Shenmai) and acupuncture combined with Western pharmacological approaches, with emphasis on mechanisms involving myocardial regeneration, contractile function improvement, ventricular remodeling reversal, and mitochondrial energy metabolism enhancement.

Answer: The traditional Chinese medicine Qiliqiangxin in heart failure with reduced ejection fraction: a randomized, double-blind, placebo-controlled trial

Answer: Qili Qiangxin ameliorates chronic heart failure: a randomized clinical trial of biomarkers, inflammation, and cardiac outcomes

Answer: The effects of qishen granules for patients with chronic heart failure: A multicenter randomized double-blind placebo-controlled trial

Answer: Shenmai Injection Improves Energy Metabolism in Patients With Heart Failure: A Randomized Controlled Trial

Answer: A randomized, double-blind, multicenter, placebo-controlled clinical study on the efficacy and safety of Shenmai injection in patients with chronic heart failure

Answer: Qiliqiangxin: A multifaceted holistic treatment for heart failure or a pharmacological probe for the identification of cardioprotective mechanisms?

Answer: Natural Metabolites Used in Traditional Chinese Medicine for Cardiovascular Diseases: Pharmacological Mechanisms, Evidence, and Future Directions

Answer: Study protocol for a randomized controlled trial: Qiliqiangxin in heart failUre: assESsment of reduction in morTality (QUEST)

Answer: Qiliqiangxin Capsules Optimize Cardiac Metabolism Flexibility in Rats With Heart Failure After Myocardial Infarction

Answer: Clinical Effect of Qili Qiangxin Capsule Combined with Sacubitril-Valsartan in Patients with Chronic Heart Failure

Answer: Qiliqiangxin Improves Cardiac Function through Regulating Energy Metabolism via HIF-1 α -Dependent and Independent Mechanisms in Heart Failure Rats after Acute Myocardial Infarction

Answer: Traditional Chinese Medicine Intervenes Ventricular Remodeling Following Acute Myocardial Infarction: Evidence From 40 Random Controlled Trials With 3, 659 Subjects

Answer: Study protocol: Traditional Chinese Medicine (TCM) syndrome differentiation for heart failure patients and its implication for long-term therapeutic outcomes of the Qiliqiangxin capsules

Answer: Efficacy of Dialectical Comprehensive Treatment of Traditional Chinese Medicine in Patients with Chronic Stable Heart Failure: A Randomized Controlled Trial

Answer: Effects of multifaceted optimization management for chronic heart failure: a multicentre, randomized controlled study

Answer: Effect of co-administration of Linggui zhugan decoction and Western medicine on inflammatory cytokines, and immune and cardiac functions of patients with chronic heart failure

Answer: Randomized, Double-Blinded, Multicenter, Placebo-Controlled Trial of Shenfu Injection for Treatment of Patients with Chronic Heart Failure during the Acute Phase of Symptom Aggravation (Yang and Qi Deficiency Syndrome)

Medicine and Life Sciences


Question: Please find papers on parameter-efficient fine-tuning (PEFT) for large language models that adopt SVD-based initialization for low-rank adapters, compare performance with LoRA/QLoRA across models of 7B-70B parameters (including LLaMA-2, Mistral, Gemma), and report results on NLG tasks (GSM8K, MATH, HumanEval) and NLU tasks (GLUE benchmark).

Answer: PiSSA: Principal Singular Values and Singular Vectors Adaptation of Large Language Models

Answer: LoRA-XS: Low-Rank Adaptation with Extremely Small Number of Parameters

Artificial Intelligence and Computing


Question: Please help locate papers that investigate the effect of SVD-based weight pruning on in-context learning (ICL) performance of Transformers, analyze layer-wise differences (shallow vs. deep layers) in pruning stability, and provide theoretical explanations via implicit gradient descent trajectories and mutual information-based generalization bounds.

Answer: Enhancing In-Context Learning Performance with just SVD-Based Weight Pruning: A Theoretical Perspective

Artificial Intelligence and Computing


Question: I'm researching deep reinforcement learning approaches for joint task scheduling and resource allocation in computing power networks. Please help me find papers that: (1) propose actor-critic based DRL methods such as A3C, PPO, DDPG, or SAC, (2) specifically address dynamic scheduling in edge-cloud collaborative environments or distributed computing power networks, (3) optimize multiple objectives including at least makespan and energy efficiency, (4) provide experimental results comparing against traditional heuristic algorithms like Round Robin, Min-Min, or HEFT, and (5) were published between January 2023 and October 2025.

Answer: Reinforcement learning methods for computation offloading: a systematic review

Answer: Enhanced DDPG algorithm for latency and energy-efficient task scheduling in MEC systems

Answer: Multi-Objective Prioritized Task Scheduler Using Improved Asynchronous Advantage Actor Critic (a3c) Algorithm in Multi Cloud Environment

Answer: Hybrid Edge-Cloud Collaborator Resource Scheduling Approach Based on Deep Reinforcement Learning and Multiobjective Optimization

Answer: TF-DDRL: A Transformer-Enhanced Distributed DRL Technique for Scheduling IoT Applications in Edge and Cloud Computing Environments

Answer: Energy-Efficient Dynamic Workflow Scheduling in Cloud Environments Using Deep Learning

Answer: FAMASO: fog-adaptive multi-agent scheduling optimization

Answer: Joint Scheduling and Placement for Vehicular Intelligent Applications Under QoS Constraints: A PPO-Based Precedence-Preserving Approach

Answer: Hybrid quantum-enhanced reinforcement learning for energy-efficient resource allocation in fog-edge computing

Answer: Research on task offloading optimization strategies for vehicular networks based on game theory and deep reinforcement learning

Answer: Energy-saving Service Offloading for the Internet of Medical Things Using Deep Reinforcement Learning

Answer: Priority-Aware Task Scheduling in Computing Power Network-Enabled Edge Computing Systems

Answer: Utility-Driven End-to-End Network Slicing for Diverse IoT Users in MEC: A Multi-Agent Deep Reinforcement Learning Approach

Answer: Multi-objective scheduling of cloud-edge cooperation in distributed manufacturing via multi-agent deep reinforcement learning

Answer: Deep Reinforcement Learning Task Scheduling Method for Real-Time Performance Awareness

Answer: Telemetry-aided cooperative multi-agent online reinforcement learning for DAG task scheduling in computing power networks

Answer: A cloud edge resources scheduling method based on reinforcement learning in industrial internet of things

Answer: Cloud–edge collaboration task scheduling in cloud manufacturing: An attention-based deep reinforcement learning approach

Answer: Analysis of Deep Reinforcement Learning Algorithms for Task Offloading and Resource Allocation in Fog Computing Environments

Answer: Research on Edge Computing and Cloud Collaborative Resource Scheduling Optimization Based on Deep Reinforcement Learning

Artificial Intelligence and Computing


Question: I need papers that use hybrid evolutionary-reinforcement learning approaches for task offloading and resource provisioning in computing power networks. The methods should combine evolutionary algorithms (like NSGA-II, MOEA/D, or SPEA2) with reinforcement learning techniques. Requirements: (1) target edge computing or computing power network scenarios with heterogeneous nodes, (2) consider at least three conflicting objectives such as latency, energy consumption, and quality of service, (3) include real-world workload traces or standard benchmarks in experiments. However, please exclude papers that focus solely on vehicular networks or IoT sensor networks, and exclude pure survey papers without proposing new methods. Papers from 2022 to October 2025 preferred.

Answer: Evolutionary Multi-Objective Deep Reinforcement Learning for Task Offloading in Industrial Internet of Things

Answer: Enhancing multi-objective evolutionary algorithms with machine learning for scheduling problems: recent advances and survey

Answer: Evolutionary Multi-Objective Reinforcement Learning Based Trajectory Control and Task Offloading in UAV-Assisted Mobile Edge Computing

Answer: Reinforcement Learning Controlled Adaptive PSO for Task Offloading in IIoT Edge Computing

Answer: QHRMOF: A Quantum-Inspired hybrid Multi-Objective framework for Energy-Efficient task scheduling and load balancing in cloud computing

Answer: Scalable and Energy-Efficient Service Orchestration in the Edge-Cloud Continuum With Multi-Objective Reinforcement Learning

Answer: A Collaborative Migration Algorithm for Edge Services Based on Evolutionary Reinforcement Learning

Answer: Learning-Based Deadlock-Free Multi-Objective Task Offloading in Satellite Edge Computing With Data-Dependent Constraints and Limited Buffers

Answer: An Intelligent Scheduling Strategy in Fog Computing System Based on Multi-Objective Deep Reinforcement Learning Algorithm

Answer: Energy–latency tradeoffs edge server selection and DQN-based resource allocation schemes in MEC

Answer: Data transmission optimization in edge computing using multi-objective reinforcement learning

Answer: Snake swarm optimization‐based deep reinforcement learning for resource allocation in edge computing environment

Answer: SPMOO: A Multi-Objective Offloading Algorithm for Dependent Tasks in IoT Cloud-Edge-End Collaboration

Answer: Deep reinforcement learning-based edge computing offloading algorithm for software-defined IoT

Answer: Reducing idleness in financial cloud services via multi-objective evolutionary reinforcement learning based load balancer

Answer: Adaptive Service Recommendation in Internet of Things Using a Reinforcement Learning and Optimization Algorithm

Answer: Energy-efficient offloading based on hybrid bio-inspired algorithm for edge-cloud integrated computation

Answer: Delay-Aware and Energy-Efficient Task Scheduling Using Strength Pareto Evolutionary Algorithm II in Fog-Cloud Computing Paradigm

Artificial Intelligence and Computing


Question: I want to do research on the relationship between neonatal diseases and facial recognition, voice recognition and bowel sound recognition, and help me search for the literature in this area in recent 10 years

Answer: CLINICAL VALIDATION OF SWAASA ARTIFICIAL INTELLIGENCE PLATFORM USING COUGH SOUNDS FOR SCREENING AND DIAGNOSIS OF RESPIRATORY DISEASES

Answer: A Comprehensive and Context-Sensitive Neonatal Pain Assessment Using Computer Vision

Answer: Disclosing neonatal pain in real-time: AI-derived pain sign from continuous assessment of facial expressions

Answer: Multimodal spatio-temporal deep learning approach for neonatal postoperative pain assessment

Answer: Attentional Generative Multimodal Network for Neonatal Postoperative Pain Estimation

Answer: Multimodal neonatal procedural and postoperative pain assessment dataset

Answer: Video-based neonatal pain assessment in uncontrolled conditions

Answer: Convolutional Neural Network for Neonatal Pain Assessment

Answer: Pain Assessment From Facial Expression: Neonatal Convolutional Neural Network (N-CNN)

Answer: Multi-Channel Neural Network for Assessing Neonatal Pain from Videos

Answer: First Investigation Into the Use of Deep Learning for Continuous Assessment of Neonatal Postoperative Pain

Answer: Enhancing Neonatal Pain Assessment Transparency via Explanatory Training Examples Identification

Answer: Explainable AI (XAI) for Neonatal Pain Assessment via Influence Function Modification

Answer: Few-Shot Prompting with Vision Language Model for Pain Classification in Infant Cry Sounds

Answer: Automated Deep Learning Approach for Post-Operative Neonatal Pain Detection and Prediction through Physiological Signals

Answer: Human vs machine towards neonatal pain assessment: A comprehensive analysis of the facial features extracted by health professionals, parents, and convolutional neural networks

Answer: Evaluating neonatal pain via fusing vision transformer and concept-cognitive computing

Answer: Automatic Infants’ Pain Assessment by Dynamic Facial Representation: Effects of Profile View, Gestational Age, Gender, and Race

Answer: Infants' Pain Recognition Based on Facial Expression: Dynamic Hybrid Descriptions

Answer: A Review of Automated Pain Assessment in Infants: Features, Classification Tasks, and Databases

Answer: Machine-based Multimodal Pain Assessment Tool for Infants: A Review

Answer: Automated Pain Assessment in Neonates

Answer: An Approach for Automated Multimodal Analysis of Infants' Pain

Answer: Neonatal pain detection in videos using the iCOPEvid dataset and an ensemble of descriptors extracted from gaussian of local descriptors

Answer: A Video Database of Neonatal Facial Expression based on Painful Clinical Procedures

Answer: Infant Cry Signal Diagnostic System Using Deep Learning and Fused Features

Answer: Newborn Cry-Based Diagnostic System to Distinguish between Sepsis and Respiratory Distress Syndrome Using Combined Acoustic Features

Answer: Deep learning systems for automatic diagnosis of infant cry signals

Answer: Neural Transfer Learning for Cry-based Diagnosis of Perinatal Asphyxia

Answer: Harnessing Infant Cry for swift, cost-effective Diagnosis of Perinatal Asphyxia in low-resource settings

Answer: Cry-based infant pathology classification using GMMs

Answer: Deep Learning Assisted Neonatal Cry Classification via Support Vector Machine Models

Answer: Deep Learning for Infant Cry Recognition

Answer: Infant Cry Classification with Graph Convolutional Networks

Answer: Infant Vocal Tract Development Analysis and Diagnosis by Cry Signals with CNN Age Classification

Answer: Ensemble Approach on Deep and Handcrafted Features for Neonatal Bowel Sound Detection

Answer: Neonatal Bowel Sound Detection Using Convolutional Neural Network and Laplace Hidden Semi-Markov Model

Answer: Machine learning-based diagnostic model for neonatal intestinal diseases in multiple centres: a cross-sectional study protocol

Answer: Comparison of simultaneous auscultation and ultrasound for clinical assessment of bowel peristalsis in neonates

Answer: Infant Facial Expression Analysis: Towards a Real-Time Video Monitoring System Using R-CNN and HMM

Answer: Multimodal Spatio-Temporal Deep Learning Approach for Neonatal Postoperative Pain Assessment

Answer: How can cry acoustics associate newborns' distress levels with neurophysiological and behavioral signals?

Answer: Multi-modal analysis of infant cry types characterization: Acoustics, body language and brain signals

Answer: Application of Voice Recognition Technology for Early Detection of Respiratory Disorders in Premature Babies

Answer: Video-based discomfort detection for infants

Answer: Analyzing neonatal vocal expression: Methological approaches to identifying neurological and psychiatric signatures

Answer: Future roles of artificial intelligence in early pain management of newborns

Answer: Application of Texture Descriptors to Facial Emotion Recognition in Infants

Answer: Comparative analysis of artificial intelligence and expert assessments in detecting neonatal procedural pain

Answer: Automatic Neonatal Pain Estimation: An Acute Pain in Neonates Database

Answer: Facial recognition accuracy in photographs of Thai neonates with Down syndrome among physicians and the Face2Gene application

Answer: Pain Assessment in Neonatal Clinical Practice Via Facial Expression Analysis and Deep Learning

Answer: Neonatal procedural pain can be assessed by computer software that has good sensitivity and specificity to detect facial movements

Answer: A self-training automatic infant-cry detector

Answer: AI FOR INFANT WELL-BEING: ADVANCED TECHNIQUES IN CRY INTERPRETATION AND MONITORING

Answer: Video-Based Versus On-Site Neonatal Pain Assessment in Neonatal Intensive Care Units: The Impact of Video-Based Neonatal Pain Assessment in Real-World Scenario on Pain Diagnosis and Its Artificial Intelligence Application

Answer: Evaluating New Set of Acoustical Features for Cry Signal Classification

Answer: Listening deeper: neural networks unravel acoustic features in preterm infant crying

Answer: Utilizing Infant Cry Acoustics to Determine Gestational Age

Answer: The significance of an infant's cry: a narrative review of physiological, pathological, and analytical perspectives

Answer: Automated Assessment of Children’s Postoperative Pain Using Computer Vision

Medicine and Life Sciences


Question: Help me find the research literature on the relationship between disease and facial recognition or voice recognition in children or newborns in the past ten years

Answer: Review on facial-recognition-based applications in disease diagnosis

Answer: Robust autism spectrum disorder screening based on facial images (for disability diagnosis): a domain-adaptive deep ensemble approach

Answer: Real-time facial emotion recognition model based on kernel autoencoder and convolutional neural network for autism children

Answer: An assessment of dynamic facial emotion recognition and theory of Mind in children with ADHD: an eye-tracking study

Answer: A Comprehensive and Context-Sensitive Neonatal Pain Assessment Using Computer Vision

Answer: Disclosing neonatal pain in real-time: AI-derived pain sign from continuous assessment of facial expressions

Answer: Multimodal spatio-temporal deep learning approach for neonatal postoperative pain assessment

Answer: Video-Based Neonatal Pain Assessment in Uncontrolled Conditions

Answer: Attentional Generative Multimodal Network for Neonatal Postoperative Pain Estimation

Answer: Multimodal neonatal procedural and postoperative pain assessment dataset

Answer: First Investigation Into the Use of Deep Learning for Continuous Assessment of Neonatal Postoperative Pain

Answer: Multi-Channel Neural Network for Assessing Neonatal Pain from Videos

Answer: Convolutional Neural Network for Neonatal Pain Assessment

Answer: Pain Assessment From Facial Expression: Neonatal Convolutional Neural Network (N-CNN)

Answer: Harnessing the Power of Deep Learning Methods in Healthcare: Neonatal Pain Assessment from Crying Sound

Answer: Video-Based versus On-Site Neonatal Pain Assessment in Neonatal Intensive Care Units: The Impact of Video-Based Neonatal Pain Assessment in Real-World Scenario on Pain Diagnosis and Its Artificial Intelligence Application

Answer: A Video Database of Neonatal Facial Expression based on Painful Clinical Procedures

Answer: A Deep Learning Approach for Infant Pain Assessment Using Facial Expressions Through Convolutional Neural Network

Answer: Automatic Infants’ Pain Assessment by Dynamic Facial Representation: Effects of Profile View, Gestational Age, Gender, and Race

Answer: Infants' Pain Recognition Based on Facial Expression: Dynamic Hybrid Descriptions

Answer: A Review of Automated Pain Assessment in Infants: Features, Classification Tasks, and Databases

Answer: Automated Pain Assessment in Neonates

Answer: An Approach for Automated Multimodal Analysis of Infants' Pain

Answer: Neonatal pain assessment: Do we have the right tools?

Answer: Face-based automatic pain assessment: challenges and perspectives in neonatal intensive care units

Answer: Neonatal Face and Facial Landmark Detection from Video Recordings

Answer: Infant Cry Signal Diagnostic System Using Deep Learning and Fused Features

Answer: Newborn Cry Acoustics in the Assessment of Neonatal Opioid Withdrawal Syndrome Using Machine Learning

Answer: Newborn Cry-Based Diagnostic System to Distinguish between Sepsis and Respiratory Distress Syndrome Using Combined Acoustic Features

Answer: Neural Transfer Learning for Cry-based Diagnosis of Perinatal Asphyxia

Answer: Harnessing Infant Cry for swift, cost-effective Diagnosis of Perinatal Asphyxia in low-resource settings

Answer: Automatic Cry Analysis: Deep Learning for Screening of Autism Spectrum Disorder in Early Childhood

Answer: A Narrative Review on Different Novel Machine Learning Techniques for Detecting Pathologies in Infants From Born Baby Cries

Answer: Cry-based infant pathology classification using GMMs

Answer: InfantCryNet: A Data-driven Framework for Intelligent Analysis of Infant Cries

Answer: A Study of Deep Learning Models for Audio Classification of Infant Crying in a Baby Monitoring System

Answer: Automated Cough Sound Analysis for Detecting Childhood Pneumonia

Answer: Detecting Childhood Pneumonia Using Handcrafted and Deep Learning Cough Sound Features and Multilayer Perceptron

Answer: iMedic: Towards Smartphone-based Self-Auscultation Tool for AI-Powered Pediatric Respiratory Assessment

Answer: A machine learning approach to the development and prospective evaluation of a pediatric lung sound classification model

Answer: Detecting acute respiratory diseases in the pediatric population using cough sound features and machine learning: A systematic review

Answer: Artificial Intelligence Models for Pediatric Lung Sound Analysis: Systematic Review and Meta-Analysis

Answer: Identifying facial phenotypes of genetic disorders using deep learning

Answer: DeepGestalt - Identifying Rare Genetic Syndromes Using Deep Learning

Answer: GestaltMatcher: Overcoming the limits of rare disease matching using facial phenotypic descriptors

Answer: Efficiency of Computer-Aided Facial Phenotyping (DeepGestalt) in Individuals With and Without a Genetic Syndrome: Diagnostic Accuracy Study

Answer: Computer-aided facial analysis as a tool to identify patients with Silver-Russell syndrome and Prader-Willi syndrome

Answer: GestaltMML: Enhancing Rare Genetic Disease Diagnosis through Multimodal Machine Learning Combining Facial Images and Clinical Texts

Answer: Association in Facial Phenotype, Gene, Disease: A Dataset for Explainable Rare Genetic Diseases Diagnosis

Answer: Automatic Detection of Autism Spectrum Disorder in Children Using Acoustic and Text Features from Brief Natural Conversations

Answer: Analysis and classification of speech sounds of children with autism spectrum disorder using acoustic features

Answer: An Acoustic Characterization of Prosodic Differences in Autism Spectrum Disorder and First-Degree Relatives

Answer: Detecting Autism Spectrum Disorders with Machine Learning Models Using Speech Transcripts

Answer: Autism Speech Analysis using Acoustic Features

Answer: Diagnosis of Autism in Children using Facial Analysis and Deep Learning

Answer: A Two-stage Multi-modal Affect Analysis Framework for Children with Autism Spectrum Disorder

Answer: Face-Based Attention Recognition Model for Children with Autism Spectrum Disorder

Answer: Real-time facial emotion recognition system among children with autism based on deep learning and IoT

Answer: Facial expression recognition is linked to clinical and neurofunctional differences in autism

Answer: Exploring Machine Learning Approaches for Diagnosing Autism Spectrum Disorder: Insights from Clinical and Technological Approaches

Answer: A Scoping Review of AI-Based Approaches for Detecting Autism Traits Using Voice and Behavioral Data

Answer: Multimodal Spatio-Temporal Deep Learning Approach for Neonatal Postoperative Pain Assessment

Answer: Deep Learning for Autism Diagnosis and Facial Analysis in Children

Answer: Application of the Face2Gene tool in an Italian dysmorphological pediatric clinic: Retrospective validation and future perspectives

Answer: A cry for help: Early detection of brain injury in newborns

Answer: Deep-Learning-Based Detection of Infants with Autism Spectrum Disorder Using Auto-Encoder Feature Representation

Answer: Early Detection of Autism Spectrum Disorder in Pediatric Populations using Facial Feature Analysis

Answer: Automated ASD detection in children from raw speech using customized STFT-CNN model

Answer: Empirical Study of Autism Spectrum Disorder Diagnosis Using Facial Images by Improved Transfer Learning Approach

Answer: Voice as a Biomarker of Pediatric Health: A Scoping Review

Answer: Screening Children's Intellectual Disabilities with Phonetic Features, Facial Phenotype and Craniofacial Variability Index

Answer: A Review of Artificial Intelligence-Based Down Syndrome Detection Techniques

Answer: Quantifying Voice Characteristics for Detecting Autism

Answer: Video-based discomfort detection for infants

Answer: Deep Learning-Powered Down Syndrome Detection Using Facial Images

Answer: Optimization and evaluation of facial recognition models for Williams-Beuren syndrome

Answer: GestaltMatcher facilitates rare disease matching using facial phenotype descriptors

Answer: Wireless broadband acousto-mechanical sensing system for continuous physiological monitoring

Answer: Future perspectives of heart rate and oxygenation monitoring in the neonatal intensive care unit – a narrative review

Answer: Feasibility and basic acoustic characteristics of intelligent long-term bowel sound analysis in term neonates

Medicine and Life Sciences


Question: Please help me find research papers published between January 2023 and November 1, 2025, on all-polymer solar cells (all-PSCs). The active layer must utilize PM6 as the polymer donor and a polymerized small molecule acceptor (PSMA) (e.g., PY-series or L-series) as the acceptor. The papers must explicitly report a Power Conversion Efficiency (PCE) exceeding 17%. Please exclude review articles.

Answer: A Refined Bulk P–I–N Structure in All-Polymer Solar Cells To Achieve 20.1% Efficiency and Improved Stability

Answer: Optimizing the power conversion processes in diluted donor/acceptor heterojunctions towards 19.4% efficiency all-polymer solar cells

Answer: Binary all-polymer solar cells with 19.30% efficiency enabled by bromodibenzothiophene-based solid additive

Answer: In-situ understanding on the formation of fibrillar morphology in green solvent processed all-polymer solar cells

Answer: 19.1% efficiency all-polymer solar cells enabled by solvent and solid additives

Answer: All-polymer organic solar cells with nano-to-micron hierarchical morphology and large light receiving angle

Answer: Delayed Crystallization Kinetics Allowing High-Efficiency All-Polymer Photovoltaics with Superior Upscaled Manufacturing

Answer: Asymmetrified Benzothiadiazole-Based Solid Additives Enable All-Polymer Solar Cells with Efficiency Over 19

Answer: Isomerized Green Solid Additive Engineering for Thermally Stable and Eco-Friendly All-Polymer Solar Cells with Approaching 19% Efficiency

Answer: An efficient alkoxy-substituted polymer acceptor for efficient all-polymer solar cells with low voltage loss and versatile photovoltaic applications

Answer: Over 18.2% efficiency of layer–by–layer all–polymer solar cells enabled by homoleptic iridium(III) carbene complex as solid additive

Answer: Binary All-polymer Solar Cells with a Perhalogenated-Thiophene-Based Solid Additive Surpass 18% Efficiency

Answer: Over 18% Efficiency of All-Polymer Solar Cells with Long-Term Stability Enabled by Y6 as a Solid Additive

Answer: Over 18% efficiency ternary all-polymer solar cells with high photocurrent and fill factor

Answer: Improved Molecular Ordering in a Ternary Blend Enables All-Polymer Solar Cells over 18% Efficiency

Answer: A nonfullerene acceptor as a solid additive realizing a record efficiency of 17.74% in quasi-layered all-polymer solar cells

Answer: Improving the aggregation of layer-by-layer processed all-polymer solar cells with an additive during the film deposition and thermal annealing

Answer: Over 17.4% Efficiency of Layer-by-Layer All-Polymer Solar Cells by Improving Exciton Utilization in Acceptor Layer

Answer: High performance all-polymer solar cells enabled with solvent and solid dual additives

Answer: Oligomeric Acceptor Enables High-Performance and Robust All-Polymer Solar Cells with 17.4% Efficiency

Answer: Binary all-polymer solar cells with efficiency over 17% by fine-tuning halogenated thiophene linkers of polymer acceptors

Answer: Over 17.1% or 18.2% Efficiency of Layer-by-Layer All-Polymer Solar Cells via Incorporating Efficient Pt Complexes as Energy Donor Additive

Answer: 18.2%-efficient ternary all-polymer organic solar cells with improved stability enabled by a chlorinated guest polymer acceptor

Answer: Manipulating Polymer Backbone Configuration Via Halogenated Asymmetric End‐Group Enables Over 18% Efficiency All‐Polymer Solar Cells

Answer: High efficiency and thermally stable ternary all-polymer solar cells achieved by alloyed vinyl-linked polymerized small-molecule acceptors

Answer: Toluene Processed All‐Polymer Solar Cells with 18% Efficiency and Enhanced Stability Enabled by Solid Additive: Comparison Between Sequential‐Processing and Blend‐Casting

Answer: Improved photovoltaic performance and robustness of all-polymer solar cells enabled by a polyfullerene guest acceptor

Answer: Non-fused Polymerized Small-Molecule Acceptors with a Benzothiadiazole Core for All-Polymer Solar Cells

Engineering and Technology


Question: I am looking for papers on n-type conjugated polymers designed for Organic Electrochemical Transistors (OECTs) application. The papers should be published before November 1, 2025. I specifically want to find novel backbone structures; therefore, you must exclude any papers where the polymer backbone is based on Naphthalene Diimide (NDI) or BBL (poly(benzimidazobenzophenanthroline)). The papers should demonstrate high electron mobility μ or μC* products.

Answer: Polyethylene glycol-decorated n-type conducting polymers with improved ion accessibility for high-performance organic electrochemical transistors

Answer: Molar Mass Improves the Performance of n-Type Organic Electrochemical Transistors

Answer: Strong Proquinoidal Acceptor Enables High-Performance Ambipolar Organic Electrochemical Transistors

Answer: Matching P- and N-type Organic Electrochemical Transistor Performance Enables a Record High-gain Complementary Inverter

Answer: Polythiophenes for High-Performance N-type Organic Electrochemical Transistors

Answer: Regiochemistry and Side-Chain Engineering Enable Efficient N-Type Mixed Conducting Polymers

Answer: On-site biosignal amplification using a single high-spin conjugated polymer

Answer: Switching p-type to high-performance n-type organic electrochemical transistors via doped state engineering

Answer: Improved Stability and Performance of an n-Type Depletion Mode Poly(benzodifurandione) Based Organic Electrochemical Transistor via Electrolyte Selection

Answer: Fused Bithiophene Imide Dimer-Based n-Type Polymers for High-Performance Organic Electrochemical Transistors

Answer: Donor Engineering for High Performance n-Type OECT Materials with Exceptional Operational Stability

Answer: Ultra-Low Threshold Voltage in N-Type Organic Electrochemical Transistors Enabled by Organic Mixed Ionic-Electronic Conductors with Dual Electron-Withdrawing Substitutions

Answer: n-Type Organic Electrochemical Transistors with High Transconductance and Stability

Answer: Revealing the Role of Polaron Distribution on the Performance of n-Type Organic Electrochemical Transistors

Answer: Synthetic Nuances to Maximize n-Type Organic Electrochemical Transistor and Thermoelectric Performance in Fused Lactam Polymers

Answer: Influence of Interaction between Electrolyte with Side-Chain Free Conjugated Polymer on the Performance of Organic Electrochemical Transistors

Answer: Acceptor Functionalization via Green Chemistry Enables High‐Performance n‐Type Organic Electrochemical Transistors for Biosensing, Memory Applications

Answer: Selenophene Substitution Enabled High‐Performance N‐Type Polymeric Mixed Ionic‐Electronic Conductors for Organic Electrochemical Transistors and Glucose Sensors

Answer: Fused Bithiophene Imide Dimer‐Based n‐Type Polymers for High‐Performance Organic Electrochemical Transistors

Answer: n ‐Type Rigid Semiconducting Polymers Bearing Oligo(Ethylene Glycol) Side Chains for High‐Performance Organic Electrochemical Transistors

Answer: Green Synthesis of Lactone‐Based Conjugated Polymers for n‐Type Organic Electrochemical Transistors

Answer: Indacenodithiophene‐based single‐component ambipolar polymer for high‐performance vertical organic electrochemical transistors and inverters

Answer: High‐Performance, Single‐Component Ambipolar Organic Electrochemical Transistors with Balanced n/p‐Type Properties for Inverter and Biosensor Applications

Answer: N-type organic electrochemical transistors with stability in water

Answer: Polythiophenes for High‐Performance N‐type Organic Electrochemical Transistors

Answer: Stable n‐type perylene derivative ladder polymer with antiambipolarity for electrically reconfigurable organic logic gates

Engineering and Technology


Question: Could you help me find some papers on hull stress field identification and wave elevation reconstruction, including full-scale ship validation experiments as well as simulations conducted via finite element or numerical methods? These methods should take hull motion signals or hull structural responses as inputs.

Answer: An Inverse FEM for Structural Health Monitoring of a Containership: Sensor Network Optimization for Accurate Displacement, Strain, and Internal Force Reconstruction

Answer: Data-Driven Estimations of Response Transfer Functions of a Marine Vessel and Phase-Resolved Incident Waves

Answer: Kalman filter technique for estimating encountered wave profiles and unmeasured ship responses using measurement data in short-crested irregular waves

Answer: Estimation of encountered wave elevation sequences based on response measurements in multi-directional seas

Answer: End-to-end deep learning method to reconstruct full-field stress distribution for ship hull structure with stress concentrations

Answer: A data-driven approach to full-field stress reconstruction of ship hull structure using deep learning

Answer: Real-time inverse estimation of multi-directional random waves from vessel-motion sensors using Kalman filter

Answer: A Laplace domain method for estimating wave elevation from the transient motion responses of floating structures

Answer: Estimation of directional sea spectra from ship motions in sea trials

Answer: Real-Time Inverse Estimation of Ocean Wave Spectra from Vessel-Motion Sensors Using Adaptive Kalman Filter

Answer: Kalman filtering of vessel motions for ocean wave directional spectrum estimation

Answer: A digital twin for ship structures—R&D project in Japan

Answer: Towards improved understanding of naval ship structural performance via virtual hull monitoring

Answer: VIRTUAL HULL MONITORING: CONTINUOUS FATIGUE ASSESSMENT WITHOUT ADDITIONAL INSTRUMENTATION

Answer: Virtual hull monitoring using hindcast and motion data to assess frigate-size vessel stress response

Answer: Virtual hull monitoring of a naval vessel using hindcast data and reconstructed 2-D wave spectra

Answer: Estimation of directional wave spectra and hull structural responses based on measured hull data on 14,000 TEU large container ships

Answer: Accuracy verification of the 2D spectral AIS method by the hull monitoring data

Answer: Response and fatigue assessment of high speed aluminium hulls using short-term wireless hull monitoring

Answer: Sea state estimation using monitoring data by convolutional neural network (CNN)

Answer: A stress component analysis method for hull structural monitoring

Answer: A reconstruction method of stress fields, bending moments and wave parameters for floating nuclear power plants through regular wave identification

Answer: Real-time prediction of wave-induced hull girder loads for a large container ship based on the recurrent neural network model and error correction strategy

Answer: Live reconstruction of global loads on a powerboat using local strain FBG measurements

Answer: A High-Precision Inverse Finite Element Method for Shape Sensing and Structural Health Monitoring

Answer: Identification of slam events experienced by a high-speed craft

Answer: Development of a Shipboard Remote Control and Telemetry Experimental System for Large-Scale Model’s Motions and Loads Measurement in Realistic Sea Waves

Engineering and Technology


Question: I am using the sensors mounted on the hull to reconstruct the hull stress field and the wave height where the ship is located. However, how to utilize the ship hull motion response and structural response has become a problem. Could you recommend some papers on how to identify the wave field and stress field using motion signals and strain signals?

Answer: An Inverse FEM for Structural Health Monitoring of a Containership: Sensor Network Optimization for Accurate Displacement, Strain, and Internal Force Reconstruction

Answer: Data-Driven Estimations of Response Transfer Functions of a Marine Vessel and Phase-Resolved Incident Waves

Answer: Estimating waves via measured ship responses

Answer: Kalman filter technique for estimating encountered wave profiles and unmeasured ship responses using measurement data in short-crested irregular waves

Answer: Estimation of encountered wave elevation sequences based on response measurements in multi-directional seas

Answer: End-to-end deep learning method to reconstruct full-field stress distribution for ship hull structure with stress concentrations

Answer: A data-driven approach to full-field stress reconstruction of ship hull structure using deep learning

Answer: Real-time inverse estimation of multi-directional random waves from vessel-motion sensors using Kalman filter

Answer: Parametric Estimation of Directional Wave Spectra from Moored FPSO Motion Data Using Optimized Artificial Neural Networks

Answer: Ship as a buoy for phase-resolved ocean wave field reconstruction and forecast

Answer: Real-Time Inverse Estimation of Ocean Wave Spectra from Vessel-Motion Sensors Using Adaptive Kalman Filter

Answer: Kalman filtering of vessel motions for ocean wave directional spectrum estimation

Answer: Wave-field reconstruction using stereo cameras on a floating platform in a synthetic environment

Answer: A reconstruction method of stress fields, bending moments and wave parameters for floating nuclear power plants through regular wave identification

Answer: Real-Time Wave Identification for Stationary Marine Units: A Deep Transfer Learning Approach With Adaptive Windowing

Answer: A digital twin for ship structures—R&D project in Japan

Answer: Deep-learning-enhanced digital twinning of complex composite structures and real-time mechanical interaction

Answer: Slam and wave load response reconstruction in high speed catamarans using transmissibility on full scale sea trials

Answer: Sea state estimation using monitoring data by convolutional neural network (CNN)

Answer: Virtual hull monitoring of a naval vessel using hindcast data and reconstructed 2-D wave spectra

Answer: Sea state identification using machine learning—A comparative study based on in-service data from a container vessel

Answer: Reconstruction of incident wave profiles based on short-time ship response measurements

Answer: Sea state estimation based on the motion data of a moored FPSO using neural networks: An evaluation with multiple draft conditions

Answer: Estimation of sea state parameters by the wave buoy analogy with comparisons to third generation spectral wave models

Answer: Data-driven simultaneous identification of the 6DOF dynamic model and wave load for a ship in waves

Answer: Sea state estimation using multiple ships simultaneously as sailing wave buoys

Answer: Data-Driven System Identification of 6-DoF Ship Motion in Waves with Neural Networks

Answer: Uncertainty-associated directional wave spectrum estimation from wave-induced ship responses using Machine Learning methods

Answer: Reconstruction and prediction of global whipping responses on a large cruise ship based on LSTM neural networks

Answer: Assessing an Improved Bayesian Model for Directional Motion Based Wave Inference

Answer: Live reconstruction of global loads on a powerboat using local strain FBG measurements

Answer: Data Assimilation-based Simultaneous Phase-Resolved Ocean Wave and Ship Motion Forecast

Answer: Virtual hull monitoring using hindcast and motion data to assess frigate-size vessel stress response

Engineering and Technology


Question: Please systematically review research works published in Computer Methods in Applied Mechanics and Engineering, Journal of Computing and Information Science in Engineering, Nature Communications, and other computational mechanics journals, as well as top-tier machine learning conferences such as NeurIPS, ICLR, ICML, and AAAI, with a focus on surrogate modeling methods in mechanics. The review should emphasize the historical development, major methodological paradigms, and the physical interpretability of these approaches.

Answer: Operator inference for non-intrusive model reduction of systems with non-polynomial nonlinear terms

Answer: Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

Answer: Fourier Neural Operator for Parametric Partial Differential Equations

Answer: Learning deep implicit Fourier neural operators (IFNOs) with applications to heterogeneous material modeling

Answer: Neural Operator induced Gaussian Process framework for probabilistic solution of parametric partial differential equations

Answer: Deep equilibrium based neural operators for steady-state PDEs

Answer: Thermodynamics-based artificial neural networks for constitutive modeling

Answer: Linearization Turns Neural Operators into Function-Valued Gaussian Processes

Answer: Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs

Answer: Meta-Auto-Decoder for Solving Parametric Partial Differential Equations

Answer: Discovering Nonlinear PDEs from Scarce Data with Physics-encoded Learning

Answer: Learning Semilinear Neural Operators: A Unified Recursive Framework For Prediction And Data Assimilation

Answer: SNN-PDE: Learning Dynamic PDEs from Data with Simplicial Neural Networks

Answer: PDE-Net: Learning PDEs from Data

Answer: Neural Ordinary Differential Equations

Answer: Machine Learning Approach to Model Order Reduction of Nonlinear Systems via Autoencoder and LSTM Networks

Answer: Learning continuous-time PDEs from sparse data with graph neural networks

Answer: Learning Mesh-Based Simulation with Graph Networks

Answer: Learning to Simulate Complex Physics with Graph Networks

Answer: Multipole Graph Neural Operator for Parametric Partial Differential Equations

Answer: Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators

Answer: Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data

Answer: Data-driven operator inference for nonintrusive projection-based model reduction

Answer: Conservative physics-informed neural networks on discrete domains for conservation laws: Applications to forward and inverse problems

Answer: Physics-Informed Neural Operator for Learning Partial Differential Equations

Answer: Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems

Answer: Physics-informed deep learning for simultaneous surrogate modeling and PDE-constrained optimization of an airfoil geometry

Answer: Constitutive model-constrained physics-informed neural networks framework for nonlinear structural seismic response prediction

Answer: Physics-data combined machine learning for parametric reduced-order modelling of nonlinear dynamical systems in small-data regimes

Answer: Comparison of Machine Learning and gPC-based proxy solutions for an efficient Bayesian identification of fracture parameters

Answer: Region-optimal Gaussian process surrogate model via Dirichlet process for cold-flow and combustion emulations

Answer: Gaussian process regression as a surrogate model for the computation of dispersion relations

Answer: Efficient uncertainty quantification of stochastic problems in CFD by combination of compressed sensing and POD-Kriging

Answer: Enhanced Kriging leave-one-out cross-validation in improving model estimation and optimization

Answer: Efficient reliability analysis with a CDA-based dimension-reduction model and polynomial chaos expansion

Answer: A radial basis function artificial neural network (RBF ANN) based method for uncertain distributed force reconstruction considering signal noises and material dispersion

Answer: Automatic network structure discovery of physics informed neural networks via knowledge distillation

Answer: Physics-informed deep learning approach for modeling crustal deformation

Answer: One-shot learning for solution operators of partial differential equations

Answer: Physics-informed learning of governing equations from scarce data

Answer: Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks and Operators in Scientific Computing: Fluid and Solid Mechanics

Answer: A Physics-Informed General Convolutional Network for the Computational Modeling of Materials with Damage

Answer: Using Physics-Informed Generative Adversarial Networks to Perform Super-Resolution for Multiphase Fluid Simulations

Answer: Physics-Informed Fully Convolutional Networks for Forward Prediction of Temperature Field and Inverse Estimation of Thermal Diffusivity

Answer: NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations

Answer: Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations

Answer: Automated constitutive modeling of isotropic hyperelasticity based on artificial neural networks

Answer: A method for interpolating on manifolds structural dynamics reduced-order models

Answer: Deep convolutional neural networks for eigenvalue problems in mechanics

Answer: Dimensional decomposition-aided metamodels for uncertainty quantification and optimization in engineering: a review

Answer: Enhancing CFD solver with Machine learning techniques

Answer: Accelerating MCMC via Kriging-based adaptive independent proposals and delayed rejection

Answer: Data-driven computational mechanics

Answer: Reduced Operator Inference for Nonlinear Partial Differential Equations

Answer: Bayesian operator inference for data-driven reduced-order modeling

Answer: Operator inference for non-intrusive model reduction with quadratic manifolds

Answer: Canonical and noncanonical Hamiltonian operator inference

Answer: Gradient preserving Operator Inference: Data-driven reduced-order models for equations with gradient structure

Answer: Physics-informed non-intrusive reduced-order modeling of parameterized dynamical systems

Answer: A physics-informed 3D surrogate model for elastic fields in polycrystals

Answer: Reduced order modeling of parametrized systems through autoencoders and SINDy approach: continuation of periodic solutions

Answer: β-Variational autoencoders and transformers for reduced-order modelling of fluid flows

Answer: Multi-fidelity physics constrained neural networks for dynamical systems

Answer: Physics-Aware Neural Implicit Solvers for multiscale, parametric PDEs with applications in heterogeneous media

Answer: The novel graph transformer-based surrogate model for learning physical systems

Answer: Physics-constrained polynomial chaos expansion for scientific machine learning and uncertainty quantification

Answer: Data Driven Computing with Noisy Material Data Sets

Answer: Data-driven computing in dynamics

Answer: Data-driven fracture mechanics

Answer: Model-free data-driven computational mechanics enhanced by tensor voting

Answer: Direct data-driven algorithms for multiscale mechanics

Answer: Data driven approach in multiphysics framework: Application to coupled electro-mechanical problems

Answer: A physics-informed GAN framework based on model-free data-driven computational mechanics

Answer: Data-driven methods for computational mechanics: A fair comparison between neural networks based and model-free approaches

Answer: Physics-Constrained Bayesian Neural Network for Bias and Variance Reduction

Answer: Multifidelity Physics-Constrained Neural Networks With Minimax Architecture

Answer: Physics-constrained bayesian neural network for fluid flow reconstruction with sparse and noisy data

Answer: Non-intrusive nonlinear model reduction via machine learning approximations to low-dimensional operators

Answer: Fem-constrained neural network-based surrogate model (FCNN-SM) for rapid structural response prediction: algorithm framework and reliability analysis applications

Answer: Surrogate modeling for radiative heat transfer using physics-informed deep neural operator networks

Answer: Physics-Informed Neural Network surrogate model for bypassing Blade Element Momentum theory in wind turbine aerodynamic load estimation

Answer: Kriging-based surrogate data-enriching artificial neural network prediction of strength and permeability of permeable cement-stabilized base

Answer: Computational homogenization of nonlinear elastic materials using neural networks

Answer: Graph neural networks for laminar flow prediction around random two-dimensional shapes

Answer: Convolutional Neural Network Surrogate Models for the Mechanical Properties of Periodic Structures

Answer: Derivative-enhanced Deep Operator Network

Answer: Feature-adjacent multi-fidelity physics-informed machine learning for partial differential equations

Answer: A practical PINN framework for multi-scale problems with multi-magnitude loss terms

Answer: Learning "best" kernels from data in Gaussian process regression. With application to aerodynamics

Answer: Surrogate-based analysis and optimization

Answer: Recent advances in surrogate-based optimization

Answer: Discovering governing equations from data by sparse identification of nonlinear dynamical systems

Engineering and Technology


Question: I am working on reduced-order surrogate modeling for mechanics problems, and currently employ a framework that combines POD-based dimensionality reduction with symbolic regression. However, the predictive performance of this approach degrades when applied to strongly nonlinear physical processes. Can you recommend research works that further integrate symbolic regression with reduced-order modeling techniques? In particular, I am interested in methods that describe the targeted mechanics problem settings, the types of datasets used, and the strengths and limitations of these approaches in handling strong nonlinearities.

Answer: A Massively Parallelizable Surrogate-Based Modeling Framework for Nonlinear Static Aeroelasticity and Structural Design

Answer: A journey from mechanistic to data-driven models in process engineering: dimensionality reduction, surrogate and hybrid approaches, and digital twins

Answer: Data Driven Techniques for Modal Decomposition and Reduced-Order Modelling of Fluids

Answer: Data-Driven Surrogate Models for Computational Fluid Dynamics

Answer: Construction of reduced order models for fluid flows using deep neural networks

Answer: Machine learning based extension of explainable model order reduction

Answer: On-the-fly adaptivity for nonlinear twoscale simulations using artificial neural networks and reduced order modeling

Answer: Deep learning-based reduced order modeling for unsteady flow dynamics and fluid-structure interaction

Answer: Discrete Physics-Informed Training for Projection-Based Reduced-Order Models with Neural Networks

Answer: Machine Learning Approach to Model Order Reduction of Nonlinear Systems via Autoencoder and LSTM Networks

Answer: Modeling nonlinear oscillator networks using physics-informed hybrid reservoir computing

Answer: Construction of reduced-order models for fluid flows using deep feedforward neural networks

Answer: Learning Nonlinear Projections for Reduced-Order Modeling of Dynamical Systems using Constrained Autoencoders

Answer: Symbolic Regression of Data-Driven Reduced Order Model Closures for Under-Resolved, Convection-Dominated Flows

Answer: Symbolic Regression-Enhanced Dynamic Wake Meandering: Fast and Physically Consistent Wind-Turbine Wake Modeling

Answer: Constrained sparse Galerkin regression

Answer: Promoting global stability in data-driven models of quadratic nonlinear dynamics

Answer: Reduced order modeling of parametrized systems through autoencoders and SINDy approach: continuation of periodic solutions

Answer: Data-driven nonlinear reduced-order modeling of unsteady fluid–structure interactions

Answer: Sparse reduced-order modeling : Sensor-based dynamics to full-state estimation

Answer: Data-Driven Reduced-Order Modeling of Phase Mixing Dynamics from Particle Kinetic Simulation

Answer: Weak-Form Latent Space Dynamics Identification

Answer: Sparse identification of nonlinear dynamics with low-dimensionalized flow representations

Answer: Sparse nonlinear models of chaotic electroconvection

Answer: Physics-constrained, low-dimensional models for MHD: First-principles and data-driven approaches, Efficient Aerodynamic Design via Data-driven Approaches

Answer: l1-based calibration of POD-Galerkin models of two-dimensional unsteady flows

Answer: Pressure Data-Driven Variational Multiscale Reduced Order Models

Answer: Data-Driven Variational Multiscale Reduced Order Models

Answer: Construction of Reduced Order Models for Fluid Flows Using Deep Feedforward Neural Networks

Answer: A data driven reduced order model of fluid flow by Auto-Encoder and self-attention deep learning methods

Answer: POD-Galerkin reduced order models and physics-informed neural networks for solving inverse problems for the Navier–Stokes equations

Answer: Machine learning closures for model order reduction of thermal fluids

Answer: A reduced order system ID approach to the modelling of nonlinear structural behavior in aeroelasticity

Answer: Physics-informed learning of governing equations from scarce data

Answer: Symbolic Learning of Interpretable Reduced-Order Models for Jumping Quadruped Robots

Answer: Data-driven modeling of the chaotic thermal convection in an annular thermosyphon

Answer: Identification of hybrid dynamic systems via a sparse regression algorithm

Answer: Data-driven discovery of partial differential equations

Answer: Discovering interpretable elastoplasticity models via the neural polynomial method enabled symbolic regressions

Answer: Discovering sparse interpretable dynamics from partial observations

Answer: Convergence of weak-SINDy Surrogate Models

Answer: Dimensionally Consistent Learning with Buckingham Pi

Answer: Using gene expression programming to discover macroscopic governing equations hidden in the data of molecular simulations

Answer: Discovering governing equations from data: Sparse identification of nonlinear dynamical systems

Answer: Robust learning from noisy, incomplete, high-dimensional experimental data via physically constrained symbolic regression

Answer: Data-driven discovery of coordinates and governing equations

Answer: Model selection for hybrid dynamical systems via sparse regression

Answer: T-SHRED: Symbolic Regression for Regularization and Model Discovery with Transformer Shallow Recurrent Decoders

Answer: Machine Learning of Nonlinear Waves: Data-Driven Methods for Computer-Assisted Discovery of Equations, Symmetries, Conservation Laws, and Integrability

Answer: Sparse Identification of Nonlinear Dynamics with Control (SINDYc)

Answer: Integrating symbolic regression with physics-informed neural networks for simulating nonlinear wave dynamics in arterial blood flow

Answer: CFD-driven Symbolic Identification of Algebraic Reynolds-Stress Models

Answer: Data-driven discovery of partial differential equation models with latent variables

Answer: Physics Informed Symbolic Networks

Answer: Extending a Physics-Based Constitutive Model using Genetic Programming

Answer: Learning physics-based reduced-order models from data using nonlinear manifolds

Answer: A review on data-driven constitutive laws for solids

Answer: Learning dynamics from coarse/noisy data with scalable symbolic regression

Engineering and Technology