bibtex stringlengths 204 2.28k | bib_dict dict | author_pub_id stringlengths 25 25 | num_citations int64 0 134 | citedby_url stringlengths 38 81 ⌀ | cites_id listlengths 1 3 ⌀ | pub_url stringlengths 32 277 ⌀ | url_related_articles stringlengths 65 65 ⌀ | embedding listlengths 768 768 | Last Updated stringdate 2025-06-14 00:00:00 2025-06-15 00:00:00 ⌀ | 2015 float64 0 4 ⌀ | 2016 float64 0 7 ⌀ | 2017 float64 0 12 ⌀ | 2018 float64 0 20 ⌀ | 2019 float64 0 25 ⌀ | 2020 float64 0 15 ⌀ | 2021 float64 0 19 ⌀ | 2022 float64 0 30 ⌀ | 2023 float64 0 31 ⌀ | 2024 float64 1 45 ⌀ | 2025 float64 1 21 ⌀ |
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@article{agrawal2025adaptivemulti-fidelityreinforcement,
abstract = {Multi-fidelity Reinforcement Learning (RL) frameworks efficiently utilize computational resources by integrating analysis models of varying accuracy and costs. The prevailing methodologies, characterized by transfer learning, human-inspired strategies, control variate techniques, and adaptive sampling, predominantly depend on a structured hierarchy of models. However, this reliance on a model hierarchy can exacerbate variance in policy learning when the underlying models exhibit heterogeneous error distributions across the design space. To address this challenge, this work proposes a novel adaptive multi-fidelity RL framework, in which multiple heterogeneous, non-hierarchical low-fidelity models are dynamically leveraged alongside a high-fidelity model to efficiently learn a high-fidelity policy. Specifically, low-fidelity policies and their experience data are adaptively used for efficient targeted learning, guided by their alignment with the high-fidelity policy. The effectiveness of the approach is demonstrated in an octocopter design optimization problem, utilizing two low-fidelity models alongside a high-fidelity simulator. The results demonstrate that the proposed approach substantially reduces variance in policy learning, leading to improved convergence and consistent high-quality solutions relative to traditional hierarchical multi-fidelity RL methods. Moreover, the framework eliminates the need for manually tuning model usage schedules, which can otherwise introduce significant computational overhead. This positions the framework as an effective variance-reduction strategy for multi-fidelity RL, while also mitigating the computational and …},
author = {Akash Agrawal and Christopher McComb},
citation = {arXiv preprint arXiv:2503.18229, 2025},
journal = {arXiv preprint arXiv:2503.18229},
pub_year = {2025},
title = {Adaptive Multi-Fidelity Reinforcement Learning for Variance Reduction in Engineering Design Optimization}
}
| {
"ENTRYTYPE": "article",
"ID": "agrawal2025adaptivemulti-fidelityreinforcement",
"abstract": "Multi-fidelity Reinforcement Learning (RL) frameworks efficiently utilize computational resources by integrating analysis models of varying accuracy and costs. The prevailing methodologies, characterized by transfer learning, human-inspired strategies, control variate techniques, and adaptive sampling, predominantly depend on a structured hierarchy of models. However, this reliance on a model hierarchy can exacerbate variance in policy learning when the underlying models exhibit heterogeneous error distributions across the design space. To address this challenge, this work proposes a novel adaptive multi-fidelity RL framework, in which multiple heterogeneous, non-hierarchical low-fidelity models are dynamically leveraged alongside a high-fidelity model to efficiently learn a high-fidelity policy. Specifically, low-fidelity policies and their experience data are adaptively used for efficient targeted learning, guided by their alignment with the high-fidelity policy. The effectiveness of the approach is demonstrated in an octocopter design optimization problem, utilizing two low-fidelity models alongside a high-fidelity simulator. The results demonstrate that the proposed approach substantially reduces variance in policy learning, leading to improved convergence and consistent high-quality solutions relative to traditional hierarchical multi-fidelity RL methods. Moreover, the framework eliminates the need for manually tuning model usage schedules, which can otherwise introduce significant computational overhead. This positions the framework as an effective variance-reduction strategy for multi-fidelity RL, while also mitigating the computational and …",
"author": "Akash Agrawal and Christopher McComb",
"citation": "arXiv preprint arXiv:2503.18229, 2025",
"conference": null,
"journal": "arXiv preprint arXiv:2503.18229",
"number": null,
"pages": null,
"pub_year": 2025,
"publisher": null,
"title": "Adaptive Multi-Fidelity Reinforcement Learning for Variance Reduction in Engineering Design Optimization",
"volume": null
} | 0P9w_S0AAAAJ:DJbcl8HfkQkC | 0 | null | null | https://arxiv.org/abs/2503.18229 | /scholar?oi=bibs&hl=en&q=related:7bQ3JuMn-hEJ:scholar.google.com/ | [
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@article{ahmed2025designbydata:,
abstract = {This special issue of the Journal of Mechanical Design showcases a collection of research articles focused on the crucial role of datasets in advancing the field of engineering design. Through these articles, we aim to underline the transformative impact of data-driven methods, which have reshaped numerous domains, and show their potential to reshape engineering design. However, the adoption of these methods in engineering faces hurdles such as the scarcity of high-quality diverse datasets. This editorial not only highlights these challenges but also offers recommendations for developing impactful design datasets and outlines a vision for their systematic use. To ensure transparency and facilitate effective utilization of these datasets, we advocate for the adoption of a standardized datasheet for design datasets in engineering design research.},
author = {Faez Ahmed and Cyril Picard and Wei Chen and Christopher Mccomb and Pingfeng Wang and Ikjin Lee and Tino Stankovic and Douglas Allaire and Stefan Menzel},
citation = {Journal of Mechanical Design, 1-8, 2025},
journal = {Journal of Mechanical Design},
pages = {1-8},
pub_year = {2025},
title = {Design by Data: Cultivating Datasets for Engineering Design}
}
| {
"ENTRYTYPE": "article",
"ID": "ahmed2025designbydata:",
"abstract": "This special issue of the Journal of Mechanical Design showcases a collection of research articles focused on the crucial role of datasets in advancing the field of engineering design. Through these articles, we aim to underline the transformative impact of data-driven methods, which have reshaped numerous domains, and show their potential to reshape engineering design. However, the adoption of these methods in engineering faces hurdles such as the scarcity of high-quality diverse datasets. This editorial not only highlights these challenges but also offers recommendations for developing impactful design datasets and outlines a vision for their systematic use. To ensure transparency and facilitate effective utilization of these datasets, we advocate for the adoption of a standardized datasheet for design datasets in engineering design research.",
"author": "Faez Ahmed and Cyril Picard and Wei Chen and Christopher Mccomb and Pingfeng Wang and Ikjin Lee and Tino Stankovic and Douglas Allaire and Stefan Menzel",
"citation": "Journal of Mechanical Design, 1-8, 2025",
"conference": null,
"journal": "Journal of Mechanical Design",
"number": null,
"pages": "1-8",
"pub_year": 2025,
"publisher": null,
"title": "Design by Data: Cultivating Datasets for Engineering Design",
"volume": null
} | 0P9w_S0AAAAJ:dBIO0h50nwkC | 0 | null | null | https://asmedigitalcollection.asme.org/mechanicaldesign/article/doi/10.1115/1.4067871/1212567 | /scholar?oi=bibs&hl=en&q=related:P0alnX32MZQJ:scholar.google.com/ | [
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@article{ezemba2025simulationvs.hallucination:,
author = {Jessica Ezemba and Christopher McComb and Conrad Tucker},
citation = {2025 IEEE Workshop on Design Automation for CPS and IoT (DESTION), 2025},
conference = {2025 IEEE Workshop on Design Automation for CPS and IoT (DESTION)},
pub_year = {2025},
title = {Simulation vs. Hallucination: Assessing Vision-Language Model Question Answering Capabilities in Engineering Simulations}
}
| {
"ENTRYTYPE": "article",
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"abstract": null,
"author": "Jessica Ezemba and Christopher McComb and Conrad Tucker",
"citation": "2025 IEEE Workshop on Design Automation for CPS and IoT (DESTION), 2025",
"conference": "2025 IEEE Workshop on Design Automation for CPS and IoT (DESTION)",
"journal": null,
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"pub_year": 2025,
"publisher": null,
"title": "Simulation vs. Hallucination: Assessing Vision-Language Model Question Answering Capabilities in Engineering Simulations",
"volume": null
} | 0P9w_S0AAAAJ:cWzG1nlazyYC | 0 | null | null | null | null | [
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@article{sulleiman2025icastthe,
author = {Mariam Sulleiman and Ramin Ahmed and Michael Kay and Guhaprasanna Manogharan and Christopher McComb},
citation = {International Conference on Engineering Design, 2025},
conference = {International Conference on Engineering Design},
pub_year = {2025},
title = {I Cast The Drains Down In Africa: AM-Augmented Casting as An Enabler for the African Manufacturing Industry}
}
| {
"ENTRYTYPE": "article",
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"abstract": null,
"author": "Mariam Sulleiman and Ramin Ahmed and Michael Kay and Guhaprasanna Manogharan and Christopher McComb",
"citation": "International Conference on Engineering Design, 2025",
"conference": "International Conference on Engineering Design",
"journal": null,
"number": null,
"pages": null,
"pub_year": 2025,
"publisher": null,
"title": "I Cast The Drains Down In Africa: AM-Augmented Casting as An Enabler for the African Manufacturing Industry",
"volume": null
} | 0P9w_S0AAAAJ:4vMrXwiscB8C | 0 | null | null | null | null | [
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@article{zhou2025human-machinecollaborationwith,
author = {Tiantian Zhou and Miaosi Dong and Pingbo Tang and Aslan Noorghasemi and Christopher McComb and Nikolas Martelaro},
citation = {ASCE International Conference on Computing in Civil Engineering (i3CE), 2025},
conference = {ASCE International Conference on Computing in Civil Engineering (i3CE)},
pub_year = {2025},
title = {Human-Machine Collaboration with Reinforcement Learning for Explaining Manual Layup Strategies in Composite Materials Handling}
}
| {
"ENTRYTYPE": "article",
"ID": "zhou2025human-machinecollaborationwith",
"abstract": null,
"author": "Tiantian Zhou and Miaosi Dong and Pingbo Tang and Aslan Noorghasemi and Christopher McComb and Nikolas Martelaro",
"citation": "ASCE International Conference on Computing in Civil Engineering (i3CE), 2025",
"conference": "ASCE International Conference on Computing in Civil Engineering (i3CE)",
"journal": null,
"number": null,
"pages": null,
"pub_year": 2025,
"publisher": null,
"title": "Human-Machine Collaboration with Reinforcement Learning for Explaining Manual Layup Strategies in Composite Materials Handling",
"volume": null
} | 0P9w_S0AAAAJ:HIFyuExEbWQC | 0 | null | null | null | null | [
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@article{kahlon2025developinganai-assisted,
author = {Yuval Kahlon and Christopher McComb and Fuji Haruyuki},
citation = {Computer-Aided Architectural Design Research in Asia, 2025},
conference = {Computer-Aided Architectural Design Research in Asia},
pub_year = {2025},
title = {Developing An AI-assisted Survey of CAAD Research: An Overview of Three Decades of Research at CAADRIA}
}
| {
"ENTRYTYPE": "article",
"ID": "kahlon2025developinganai-assisted",
"abstract": null,
"author": "Yuval Kahlon and Christopher McComb and Fuji Haruyuki",
"citation": "Computer-Aided Architectural Design Research in Asia, 2025",
"conference": "Computer-Aided Architectural Design Research in Asia",
"journal": null,
"number": null,
"pages": null,
"pub_year": 2025,
"publisher": null,
"title": "Developing An AI-assisted Survey of CAAD Research: An Overview of Three Decades of Research at CAADRIA",
"volume": null
} | 0P9w_S0AAAAJ:IRz6iEL74y4C | 0 | null | null | null | /scholar?oi=bibs&hl=en&q=related:fButXi5auuYJ:scholar.google.com/ | [
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0.223897591233253... | 2025-06-14 | null | null | null | null | null | null | null | null | null | null | null |
@article{chen2025multi-latticetopologyoptimization,
abstract = {Additive manufacturing enables the fabrication of multi-lattice structures, an advanced design approach featuring heterogeneous lattices at the mesoscale which are arranged to achieve a diverse and purposeful distribution of material properties at the macroscale. Compared to uniform lattice structures, multi-lattice structures permit greater design freedom and a larger design space, which makes it possible to achieve superior structure performance. However, the expanded design space introduces a substantial increase in the complexity that must be managed in order to achieve a multi-lattice structure solution. However, there is a lack of design automation approaches that can tractably create multilattice structures. This article introduces an innovative multi-scale topology optimization (TO) framework, called multi-lattice topology optimization with variational autoencoder (MulaTOVA), that is capable of concurrently …},
author = {Jiangce Chen and Martha Baldwin and Sneha Narra and Christopher McComb},
citation = {Journal of Mechanical Design, 2025},
journal = {Journal of Mechanical Design},
pub_year = {2025},
title = {Multi-lattice Topology Optimization via Generative Lattice Modeling}
}
| {
"ENTRYTYPE": "article",
"ID": "chen2025multi-latticetopologyoptimization",
"abstract": "Additive manufacturing enables the fabrication of multi-lattice structures, an advanced design approach featuring heterogeneous lattices at the mesoscale which are arranged to achieve a diverse and purposeful distribution of material properties at the macroscale. Compared to uniform lattice structures, multi-lattice structures permit greater design freedom and a larger design space, which makes it possible to achieve superior structure performance. However, the expanded design space introduces a substantial increase in the complexity that must be managed in order to achieve a multi-lattice structure solution. However, there is a lack of design automation approaches that can tractably create multilattice structures. This article introduces an innovative multi-scale topology optimization (TO) framework, called multi-lattice topology optimization with variational autoencoder (MulaTOVA), that is capable of concurrently …",
"author": "Jiangce Chen and Martha Baldwin and Sneha Narra and Christopher McComb",
"citation": "Journal of Mechanical Design, 2025",
"conference": null,
"journal": "Journal of Mechanical Design",
"number": null,
"pages": null,
"pub_year": 2025,
"publisher": null,
"title": "Multi-lattice Topology Optimization via Generative Lattice Modeling",
"volume": null
} | 0P9w_S0AAAAJ:DUooU5lO8OsC | 0 | null | null | https://asmedigitalcollection.asme.org/mechanicaldesign/article-pdf/147/5/051707/7430300/md_147_5_051707.pdf | /scholar?oi=bibs&hl=en&q=related:iYFie6KuG4IJ:scholar.google.com/ | [
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... | 2025-06-14 | null | null | null | null | null | null | null | null | null | null | null |
@article{noorghasemi2024evaluatingcommunitywell-being,
abstract = {Infrastructure provides an intricate system through which humans interact with each other and their environment, which is essential for community well-being. These systems, however, degrade over time and become less usable, either due to wear or abrupt damage. Therefore, it becomes necessary to allocate resources to maintain the infrastructure and ensure the community's well-being. However, the relationship between infrastructure expenditures and community well-being is unclear and challenging to measure. This is especially true in locations like Utqiagvik, Alaska, where permafrost thaw due to global warming threatens a substantial amount of infrastructure. Current computational models are inadequate for predicting the cascading effects of system failures, particularly under extreme environmental conditions. This paper introduces agent-based modeling (ABM) as a more adaptive and insightful method to study the impact of infrastructure on community well-being. This approach provides a detailed analysis of component contributions to system robustness, identifying key vulnerabilities for prioritized maintenance and resource allocation.},
author = {Aslan Noorghasemi and Ming Xiao and Christopher McComb},
citation = {AGU24, 2024},
journal = {AGU24},
pub_year = {2024},
publisher = {AGU},
title = {Evaluating Community Well-being and Resilience via Agent-Based Models}
}
| {
"ENTRYTYPE": "article",
"ID": "noorghasemi2024evaluatingcommunitywell-being",
"abstract": "Infrastructure provides an intricate system through which humans interact with each other and their environment, which is essential for community well-being. These systems, however, degrade over time and become less usable, either due to wear or abrupt damage. Therefore, it becomes necessary to allocate resources to maintain the infrastructure and ensure the community's well-being. However, the relationship between infrastructure expenditures and community well-being is unclear and challenging to measure. This is especially true in locations like Utqiagvik, Alaska, where permafrost thaw due to global warming threatens a substantial amount of infrastructure. Current computational models are inadequate for predicting the cascading effects of system failures, particularly under extreme environmental conditions. This paper introduces agent-based modeling (ABM) as a more adaptive and insightful method to study the impact of infrastructure on community well-being. This approach provides a detailed analysis of component contributions to system robustness, identifying key vulnerabilities for prioritized maintenance and resource allocation.",
"author": "Aslan Noorghasemi and Ming Xiao and Christopher McComb",
"citation": "AGU24, 2024",
"conference": null,
"journal": "AGU24",
"number": null,
"pages": null,
"pub_year": 2024,
"publisher": "AGU",
"title": "Evaluating Community Well-being and Resilience via Agent-Based Models",
"volume": null
} | 0P9w_S0AAAAJ:2VqYfGB8ITEC | 0 | null | null | https://essopenarchive.org/doi/full/10.22541/essoar.173687385.52831340 | /scholar?oi=bibs&hl=en&q=related:VUUtDFnw4JkJ:scholar.google.com/ | [
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@article{chen2024enforcingtheprinciple,
abstract = {Time-dependent partial differential equations (PDEs) for classic physical systems are established based on the conservation of mass, momentum, and energy, which are ubiquitous in scientific and engineering applications. These PDEs are strictly local-dependent according to the principle of locality in physics, which means that the evolution at a point is only influenced by the neighborhood around it whose size is determined by the length of timestep multiplied with the speed of characteristic information traveling in the system. However, deep learning architecture cannot strictly enforce the local-dependency as it inevitably increases the scope of information to make local predictions as the number of layers increases. Under limited training data, the extra irrelevant information results in sluggish convergence and compromised generalizability. This paper aims to solve this problem by proposing a data decomposition method to strictly limit the scope of information for neural operators making local predictions, which is called data decomposition enforcing local-dependency (DDELD). The numerical experiments over multiple physical phenomena show that DDELD significantly accelerates training convergence and reduces test errors of benchmark models on large-scale engineering simulations.},
author = {Jiangce Chen and Wenzhuo Xu and Zeda Xu and Noelia Grande Gutiérrez and Sneha Prabha Narra and Christopher McComb},
citation = {arXiv preprint arXiv:2405.01319, 2024},
journal = {arXiv preprint arXiv:2405.01319},
pub_year = {2024},
title = {Enforcing the Principle of Locality for Physical Simulations with Neural Operators}
}
| {
"ENTRYTYPE": "article",
"ID": "chen2024enforcingtheprinciple",
"abstract": "Time-dependent partial differential equations (PDEs) for classic physical systems are established based on the conservation of mass, momentum, and energy, which are ubiquitous in scientific and engineering applications. These PDEs are strictly local-dependent according to the principle of locality in physics, which means that the evolution at a point is only influenced by the neighborhood around it whose size is determined by the length of timestep multiplied with the speed of characteristic information traveling in the system. However, deep learning architecture cannot strictly enforce the local-dependency as it inevitably increases the scope of information to make local predictions as the number of layers increases. Under limited training data, the extra irrelevant information results in sluggish convergence and compromised generalizability. This paper aims to solve this problem by proposing a data decomposition method to strictly limit the scope of information for neural operators making local predictions, which is called data decomposition enforcing local-dependency (DDELD). The numerical experiments over multiple physical phenomena show that DDELD significantly accelerates training convergence and reduces test errors of benchmark models on large-scale engineering simulations.",
"author": "Jiangce Chen and Wenzhuo Xu and Zeda Xu and Noelia Grande Gutiérrez and Sneha Prabha Narra and Christopher McComb",
"citation": "arXiv preprint arXiv:2405.01319, 2024",
"conference": null,
"journal": "arXiv preprint arXiv:2405.01319",
"number": null,
"pages": null,
"pub_year": 2024,
"publisher": null,
"title": "Enforcing the Principle of Locality for Physical Simulations with Neural Operators",
"volume": null
} | 0P9w_S0AAAAJ:QYdC8u9Cj1oC | 0 | null | null | https://arxiv.org/abs/2405.01319 | /scholar?oi=bibs&hl=en&q=related:a9bqiekiL_gJ:scholar.google.com/ | [
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@article{noorghasemi2025keepdelta:apython,
abstract = {Efficiently managing evolving data is crucial in applications like computational simulations and sensing, where dynamic data tracking and processing are essential. In simulations, the traditional method known as full-state encoding stores the entire system state, including all nested data structures and variable values, at every timestep. While simple to implement, this approach is highly storage-intensive. On the other hand, recalculating states from scratch to avoid storage demands is computationally expensive. Similarly, in sensing, continuously transmitting full data snapshots is inefficient, leading to increased bandwidth consumption and latency. KeepDelta addresses this challenge by providing a lightweight Python library that captures and applies only the changes (deltas) between successive states of complex, nested Python data structures. Designed for clarity and ease of use, KeepDelta produces human-readable outputs, facilitating debugging and analysis in research applications.},
author = {Aslan Noorghasemi and Christopher McComb},
citation = {Journal of Open Source Software, 2025},
journal = {Journal of Open Source Software},
pub_year = {2025},
title = {KeepDelta: A Python Library for Human-Readable Data Differencing}
}
| {
"ENTRYTYPE": "article",
"ID": "noorghasemi2025keepdelta:apython",
"abstract": "Efficiently managing evolving data is crucial in applications like computational simulations and sensing, where dynamic data tracking and processing are essential. In simulations, the traditional method known as full-state encoding stores the entire system state, including all nested data structures and variable values, at every timestep. While simple to implement, this approach is highly storage-intensive. On the other hand, recalculating states from scratch to avoid storage demands is computationally expensive. Similarly, in sensing, continuously transmitting full data snapshots is inefficient, leading to increased bandwidth consumption and latency. KeepDelta addresses this challenge by providing a lightweight Python library that captures and applies only the changes (deltas) between successive states of complex, nested Python data structures. Designed for clarity and ease of use, KeepDelta produces human-readable outputs, facilitating debugging and analysis in research applications.",
"author": "Aslan Noorghasemi and Christopher McComb",
"citation": "Journal of Open Source Software, 2025",
"conference": null,
"journal": "Journal of Open Source Software",
"number": null,
"pages": null,
"pub_year": 2025,
"publisher": null,
"title": "KeepDelta: A Python Library for Human-Readable Data Differencing",
"volume": null
} | 0P9w_S0AAAAJ:LhH-TYMQEocC | 0 | null | null | https://joss.theoj.org/papers/10.21105/joss.08075.pdf | /scholar?oi=bibs&hl=en&q=related:7faVMrHUjscJ:scholar.google.com/ | [
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author = {Jiangce Chen and Wenzhuo Xu and Zeda Xu and Noelia Grande Gutierrez and Sneha Prabha Narra and Christopher McComb},
citation = {Journal of Computational Physics, 2025},
journal = {Journal of Computational Physics},
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title = {Enforcing the Principle of Locality for Physical Simulations with Neural Operators}
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@article{ezemba2025neuralnetworksurrogate,
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title = {Neural Network Surrogate Modeling for Stochastic Fem Using 3d Graph Representations: A Comparative Study}
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citation = {International Design Engineering Technical Conferences and Computers and …, 2025},
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title = {I Love ClaMP: A Tunable Optimization-Based Algorithm for Point Cloud Cleanup}
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citation = {International Design Engineering Technical Conferences and Computers and …, 2025},
conference = {International Design Engineering Technical Conferences and Computers and Information in Engineering Conference},
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title = {The Impact of Design Representation on Equal Contribution in Engineering Design Teams}
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@article{mcgee2025areal-timeautomatic,
author = {Elizabeth S McGee and Eric Brubaker and Jonathan Cagan and Christopher McComb},
citation = {International Design Engineering Technical Conferences and Computers and …, 2025},
conference = {International Design Engineering Technical Conferences and Computers and Information in Engineering Conference},
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@article{ying2025evaluatingtherole,
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conference = {International Design Engineering Technical Conferences and Computers and Information in Engineering Conference},
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title = {Evaluating the Role of Model Size in Agentic AI for Expert-Like Material Selection}
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@article{xu0energy-basedfeatureextraction,
abstract = {Machine learning (ML) based surrogate models offer the potential to accelerate real-world engineering simulations involving millions of elements by bypassing the need for full-scale numerical simulations. However, current model capacities and available GPU memory often impose severe constraints, limiting our ability to accurately represent the highly variant physical dynamics encountered in complex systems. In traditional numerical methods, these computational limitations are mitigated using domain decomposition. The computational domain is split up to enable parallelization of the computation and reduce memory load. Similarly, ML models can benefit from decomposing the domain into subdomains. However, domain decomposition alone is insufficient to guarantee model performance and accuracy when physical dynamics vary spatially. We introduce the Adaptive Local Domain Decomposition (ALDD) method, which features two key innovations. First, it utilizes domain decomposition to improve training and inference efficiency of the ML model, with time reduction scaling almost linearly with the number of parallel GPUs. Second, ALDD adopts adaptive domain scheduling to segment the physics domain into subdomains based on physical dynamics features. Different ML models explicitly trained to solve different physical dynamics are then applied to these subdomains, encoding boundary information to ensure a smooth transition at the subdomain interface. This is accomplished by analyzing the energy spectrum of each subdomain and applying k-means clustering on the Wasserstein distances to identify physically coherent regions. We …},
author = {Wenzhuo Xu and Madhav Karthikeyakannan and Christopher McComb and Noelia Grande Gutierrez},
citation = {Available at SSRN 5243761, 0},
journal = {Available at SSRN 5243761},
title = {Energy-Based Feature Extraction with Adaptive Local Domain Decomposition for Prediction of Transient and Turbulence Flow with Operator Regression Models}
}
| {
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"abstract": "Machine learning (ML) based surrogate models offer the potential to accelerate real-world engineering simulations involving millions of elements by bypassing the need for full-scale numerical simulations. However, current model capacities and available GPU memory often impose severe constraints, limiting our ability to accurately represent the highly variant physical dynamics encountered in complex systems. In traditional numerical methods, these computational limitations are mitigated using domain decomposition. The computational domain is split up to enable parallelization of the computation and reduce memory load. Similarly, ML models can benefit from decomposing the domain into subdomains. However, domain decomposition alone is insufficient to guarantee model performance and accuracy when physical dynamics vary spatially. We introduce the Adaptive Local Domain Decomposition (ALDD) method, which features two key innovations. First, it utilizes domain decomposition to improve training and inference efficiency of the ML model, with time reduction scaling almost linearly with the number of parallel GPUs. Second, ALDD adopts adaptive domain scheduling to segment the physics domain into subdomains based on physical dynamics features. Different ML models explicitly trained to solve different physical dynamics are then applied to these subdomains, encoding boundary information to ensure a smooth transition at the subdomain interface. This is accomplished by analyzing the energy spectrum of each subdomain and applying k-means clustering on the Wasserstein distances to identify physically coherent regions. We …",
"author": "Wenzhuo Xu and Madhav Karthikeyakannan and Christopher McComb and Noelia Grande Gutierrez",
"citation": "Available at SSRN 5243761, 0",
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"title": "Energy-Based Feature Extraction with Adaptive Local Domain Decomposition for Prediction of Transient and Turbulence Flow with Operator Regression Models",
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@article{lapp2019kaboom:anagent-based,
abstract = {The performance of a design team is influenced by each team member’s unique cognitive style – i.e., their preferred manner of managing structure as they solve problems, make decisions, and seek to bring about change. Cognitive style plays an important role in how teams of engineers design and collaborate, but the interactions of cognitive style with team organization and processes have not been well studied. The limitations of small-scale behavioral experiments have led researchers to develop computational models for simulating teamwork; however, none have modeled the effects of individuals’ cognitive styles. This paper presents the Kirton Adaption–Innovation Inventory agent-based organizational optimization model (KABOOM), the first agent-based model of teamwork to incorporate cognitive style. In KABOOM, heterogeneous agents imitate the diverse problem-solving styles described by the Kirton …},
author = {Samuel Lapp and Kathryn Jablokow and Christopher McComb},
citation = {Design Science, 2019},
journal = {Design Science},
pub_year = {2019},
title = {KABOOM: An Agent-Based Model for Simulating Cognitive Style in Team Problem Solving}
}
| {
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"abstract": "The performance of a design team is influenced by each team member’s unique cognitive style – i.e., their preferred manner of managing structure as they solve problems, make decisions, and seek to bring about change. Cognitive style plays an important role in how teams of engineers design and collaborate, but the interactions of cognitive style with team organization and processes have not been well studied. The limitations of small-scale behavioral experiments have led researchers to develop computational models for simulating teamwork; however, none have modeled the effects of individuals’ cognitive styles. This paper presents the Kirton Adaption–Innovation Inventory agent-based organizational optimization model (KABOOM), the first agent-based model of teamwork to incorporate cognitive style. In KABOOM, heterogeneous agents imitate the diverse problem-solving styles described by the Kirton …",
"author": "Samuel Lapp and Kathryn Jablokow and Christopher McComb",
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"pub_year": 2019,
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"title": "KABOOM: An Agent-Based Model for Simulating Cognitive Style in Team Problem Solving",
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@article{mccomb2015liftingtheveil:,
abstract = {Novel design methodologies are often evaluated through studies involving human designers, but such studies can incur a high personnel cost. It can also be difficult to isolate the effects of specific team or individual characteristics. This work introduces the Cognitively-Inspired Simulated Annealing Teams (CISAT) modeling framework, a platform for efficiently simulating and analyzing human design teams. The framework models a number of empirically demonstrated cognitive phenomena …},
author = {Christopher McComb and Jonathan Cagan and Kenneth Kotovsky},
citation = {Design Studies 40, 119-142, 2015},
journal = {Design Studies},
pages = {119-142},
pub_year = {2015},
publisher = {Elsevier},
title = {Lifting the Veil: Drawing Insights about Design Teams from a Cognitively-inspired Computational Model},
volume = {40}
}
| {
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"abstract": "Novel design methodologies are often evaluated through studies involving human designers, but such studies can incur a high personnel cost. It can also be difficult to isolate the effects of specific team or individual characteristics. This work introduces the Cognitively-Inspired Simulated Annealing Teams (CISAT) modeling framework, a platform for efficiently simulating and analyzing human design teams. The framework models a number of empirically demonstrated cognitive phenomena …",
"author": "Christopher McComb and Jonathan Cagan and Kenneth Kotovsky",
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"title": "Lifting the Veil: Drawing Insights about Design Teams from a Cognitively-inspired Computational Model",
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@article{raina2019learningtodesign,
abstract = {Humans as designers have quite versatile problem-solving strategies. Computer agents on the other hand can access large-scale computational resources to solve certain design problems. Hence, if agents can learn from human behavior, a synergetic human-agent problem-solving team can be created. This paper presents an approach to extract human design strategies and implicit rules, purely from historical human data, and use that for design generation. A two-step framework that learns to imitate human design strategies from observation is proposed and implemented. This framework makes use of deep learning constructs to learn to generate designs without any explicit information about objective and performance metrics. The framework is designed to interact with the problem through a visual interface as humans did when solving the problem. It is trained to imitate a set of human designers by …},
author = {Ayush Raina and Christopher McComb and Jonathan Cagan},
citation = {Journal of Mechanical Design, 2019},
journal = {Journal of Mechanical Design},
pub_year = {2019},
title = {Learning To Design From Humans: Imitating Human Designers Through Deep Learning}
}
| {
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"abstract": "Humans as designers have quite versatile problem-solving strategies. Computer agents on the other hand can access large-scale computational resources to solve certain design problems. Hence, if agents can learn from human behavior, a synergetic human-agent problem-solving team can be created. This paper presents an approach to extract human design strategies and implicit rules, purely from historical human data, and use that for design generation. A two-step framework that learns to imitate human design strategies from observation is proposed and implemented. This framework makes use of deep learning constructs to learn to generate designs without any explicit information about objective and performance metrics. The framework is designed to interact with the problem through a visual interface as humans did when solving the problem. It is trained to imitate a set of human designers by …",
"author": "Ayush Raina and Christopher McComb and Jonathan Cagan",
"citation": "Journal of Mechanical Design, 2019",
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"title": "Learning To Design From Humans: Imitating Human Designers Through Deep Learning",
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@article{zhang2021acautionarytale,
abstract = {Recent advances in artificial intelligence (AI) offer opportunities for integrating AI into human design teams. Although various AIs have been developed to aid engineering design, the impact of AI usage on human design teams has received scant research attention. This research assesses the impact of a deep learning AI on distributed human design teams through a human subject study that includes an abrupt problem change. The results demonstrate that, for this study, the AI boosts the initial performance of low-performing teams before …},
author = {Guanglu Zhang and Ayush Raina and Jonathan Cagan and Christopher McComb},
citation = {Design Studies 72, 100990, 2021},
journal = {Design Studies},
pages = {100990},
pub_year = {2021},
publisher = {Elsevier},
title = {A cautionary tale about the impact of AI on human design teams},
volume = {72}
}
| {
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"abstract": "Recent advances in artificial intelligence (AI) offer opportunities for integrating AI into human design teams. Although various AIs have been developed to aid engineering design, the impact of AI usage on human design teams has received scant research attention. This research assesses the impact of a deep learning AI on distributed human design teams through a human subject study that includes an abrupt problem change. The results demonstrate that, for this study, the AI boosts the initial performance of low-performing teams before …",
"author": "Guanglu Zhang and Ayush Raina and Jonathan Cagan and Christopher McComb",
"citation": "Design Studies 72, 100990, 2021",
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"title": "A cautionary tale about the impact of AI on human design teams",
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@article{williams2019designrepositoryeffectiveness,
abstract = {Machine learning can be used to automate common or time-consuming engineering tasks for which sufficient data already exist. For instance, design repositories can be used to train deep learning algorithms to assess component manufacturability; however, methods to determine the suitability of a design repository for use with machine learning do not exist. We provide an initial investigation toward identifying such a method using “artificial” design repositories to experimentally test the extent to which altering properties of the dataset impacts the assessment precision and generalizability of neural networks trained on the data. For this experiment, we use a 3D convolutional neural network to estimate quantitative manufacturing metrics directly from voxel-based component geometries. Additive manufacturing (AM) is used as a case study because of the recent growth of AM-focused design repositories such as …},
author = {Glen Williams and Nicholas A Meisel and Timothy W Simpson and Christopher McComb},
citation = {Journal of Mechanical Design, 2019},
journal = {Journal of Mechanical Design},
pub_year = {2019},
title = {Design Repository Effectiveness for 3D Convolutional Neural Networks: Application to Additive Manufacturing}
}
| {
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"abstract": "Machine learning can be used to automate common or time-consuming engineering tasks for which sufficient data already exist. For instance, design repositories can be used to train deep learning algorithms to assess component manufacturability; however, methods to determine the suitability of a design repository for use with machine learning do not exist. We provide an initial investigation toward identifying such a method using “artificial” design repositories to experimentally test the extent to which altering properties of the dataset impacts the assessment precision and generalizability of neural networks trained on the data. For this experiment, we use a 3D convolutional neural network to estimate quantitative manufacturing metrics directly from voxel-based component geometries. Additive manufacturing (AM) is used as a case study because of the recent growth of AM-focused design repositories such as …",
"author": "Glen Williams and Nicholas A Meisel and Timothy W Simpson and Christopher McComb",
"citation": "Journal of Mechanical Design, 2019",
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"journal": "Journal of Mechanical Design",
"number": null,
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"pub_year": 2019,
"publisher": null,
"title": "Design Repository Effectiveness for 3D Convolutional Neural Networks: Application to Additive Manufacturing",
"volume": null
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@article{mccomb2017optimizingdesignteams,
abstract = {The performance of a team with the right characteristics can exceed the mere sum of the constituent members' individual efforts. However, a team having the wrong characteristics may perform more poorly than the sum of its individuals. Therefore, it is vital that teams are assembled and managed properly in order to maximize performance. This work examines how the properties of configuration design problems can be leveraged to select the best values for team characteristics (specifically team size and interaction frequency). A computational model of design teams which has been shown to effectively emulate human team behavior is employed to pinpoint optimized team characteristics for solving a variety of configuration design problems. These configuration design problems are characterized with respect to the local and global structure of the design space, the alignment between objectives, and the resources …},
author = {Christopher McComb and Jonathan Cagan and Kenneth Kotovsky},
citation = {Journal of Mechanical Design, 2017},
journal = {Journal of Mechanical Design},
pub_year = {2017},
title = {Optimizing Design Teams Based on Problem Properties: Computational Team Simulations and an Applied Empirical Test}
}
| {
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"abstract": "The performance of a team with the right characteristics can exceed the mere sum of the constituent members' individual efforts. However, a team having the wrong characteristics may perform more poorly than the sum of its individuals. Therefore, it is vital that teams are assembled and managed properly in order to maximize performance. This work examines how the properties of configuration design problems can be leveraged to select the best values for team characteristics (specifically team size and interaction frequency). A computational model of design teams which has been shown to effectively emulate human team behavior is employed to pinpoint optimized team characteristics for solving a variety of configuration design problems. These configuration design problems are characterized with respect to the local and global structure of the design space, the alignment between objectives, and the resources …",
"author": "Christopher McComb and Jonathan Cagan and Kenneth Kotovsky",
"citation": "Journal of Mechanical Design, 2017",
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"journal": "Journal of Mechanical Design",
"number": null,
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"pub_year": 2017,
"publisher": null,
"title": "Optimizing Design Teams Based on Problem Properties: Computational Team Simulations and an Applied Empirical Test",
"volume": null
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@article{mccomb2015rollingwiththe,
abstract = {Designers must often create solutions to problems that exhibit dynamic characteristics. For instance, a client might modify specifications after design has commenced, or a competitor may introduce a new technology or feature. This paper presents a cognitive study that was conducted to explore the manner in which design teams respond to such situations. In the study, teams of undergraduate engineering students sought to solve a design task that was subject to two large, unexpected changes in problem formulation that were introduced during solving. High-and low-performing teams demonstrated very different …},
author = {Christopher McComb and Jonathan Cagan and Kenneth Kotovsky},
citation = {Design Studies 36, 99-121, 2015},
journal = {Design Studies},
pages = {99-121},
pub_year = {2015},
publisher = {Elsevier},
title = {Rolling with the Punches: An Examination of Team Performance in a Design Task Subject to Drastic Changes},
volume = {36}
}
| {
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"abstract": "Designers must often create solutions to problems that exhibit dynamic characteristics. For instance, a client might modify specifications after design has commenced, or a competitor may introduce a new technology or feature. This paper presents a cognitive study that was conducted to explore the manner in which design teams respond to such situations. In the study, teams of undergraduate engineering students sought to solve a design task that was subject to two large, unexpected changes in problem formulation that were introduced during solving. High-and low-performing teams demonstrated very different …",
"author": "Christopher McComb and Jonathan Cagan and Kenneth Kotovsky",
"citation": "Design Studies 36, 99-121, 2015",
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"journal": "Design Studies",
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"pages": "99-121",
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"publisher": "Elsevier",
"title": "Rolling with the Punches: An Examination of Team Performance in a Design Task Subject to Drastic Changes",
"volume": "36"
} | 0P9w_S0AAAAJ:WF5omc3nYNoC | 93 | /scholar?hl=en&cites=4707453472868866956 | [
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@article{mccomb2017capturinghumansequence-learning,
abstract = {Designers often search for new solutions by iteratively adapting a current design. By engaging in this search, designers not only improve solution quality but also begin to learn what operational patterns might improve the solution in future iterations. Previous work in psychology has demonstrated that humans can fluently and adeptly learn short operational sequences that aid problem-solving. This paper explores how designers learn and employ sequences within the realm of engineering design. Specifically, this work analyzes behavioral patterns in two human studies in which participants solved configuration design problems. Behavioral data from the two studies are first analyzed using Markov chains to determine how much representation complexity is necessary to quantify the sequential patterns that designers employ during solving. It is discovered that first-order Markov chains are capable of accurately …},
author = {Christopher McComb and Jonathan Cagan and Kenneth Kotovsky},
citation = {Journal of Mechanical Design 139 (9), 2017},
journal = {Journal of Mechanical Design},
number = {9},
pub_year = {2017},
publisher = {ASME},
title = {Capturing Human Sequence-Learning Abilities in Configuration Design Tasks through Markov Chains},
volume = {139}
}
| {
"ENTRYTYPE": "article",
"ID": "mccomb2017capturinghumansequence-learning",
"abstract": "Designers often search for new solutions by iteratively adapting a current design. By engaging in this search, designers not only improve solution quality but also begin to learn what operational patterns might improve the solution in future iterations. Previous work in psychology has demonstrated that humans can fluently and adeptly learn short operational sequences that aid problem-solving. This paper explores how designers learn and employ sequences within the realm of engineering design. Specifically, this work analyzes behavioral patterns in two human studies in which participants solved configuration design problems. Behavioral data from the two studies are first analyzed using Markov chains to determine how much representation complexity is necessary to quantify the sequential patterns that designers employ during solving. It is discovered that first-order Markov chains are capable of accurately …",
"author": "Christopher McComb and Jonathan Cagan and Kenneth Kotovsky",
"citation": "Journal of Mechanical Design 139 (9), 2017",
"conference": null,
"journal": "Journal of Mechanical Design",
"number": "9",
"pages": null,
"pub_year": 2017,
"publisher": "ASME",
"title": "Capturing Human Sequence-Learning Abilities in Configuration Design Tasks through Markov Chains",
"volume": "139"
} | 0P9w_S0AAAAJ:QIV2ME_5wuYC | 77 | /scholar?hl=en&cites=6286221499622255408 | [
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] | https://asmedigitalcollection.asme.org/mechanicaldesign/article-abstract/139/9/091101/383821 | /scholar?oi=bibs&hl=en&q=related:MGto6CUlPVcJ:scholar.google.com/ | [
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@article{mccomb2017miningprocessheuristics,
abstract = {Configuration design problems, characterized by the assembly of components into a final desired solution, are common in engineering design. Various theoretical approaches have been offered for solving configuration type problems, but few studies have examined the approach that humans naturally use to solve such problems. This work applies data-mining techniques to quantitatively study the processes that designers use to solve configuration design problems. The guiding goal is to extract beneficial design process heuristics that are generalizable to the entire class of problems. The extraction of these human problem-solving heuristics is automated through the application of hidden Markov models to the data from two behavioral studies. Results show that designers proceed through four procedural states in solving configuration design problems, roughly transitioning from topology design to shape and …},
author = {Christopher McComb and Jonathan Cagan and Kenneth Kotovsky},
citation = {Journal of Mechanical Design, 2017},
journal = {Journal of Mechanical Design},
pub_year = {2017},
publisher = {ASME},
title = {Mining Process Heuristics from Designer Action Data via Hidden Markov Models}
}
| {
"ENTRYTYPE": "article",
"ID": "mccomb2017miningprocessheuristics",
"abstract": "Configuration design problems, characterized by the assembly of components into a final desired solution, are common in engineering design. Various theoretical approaches have been offered for solving configuration type problems, but few studies have examined the approach that humans naturally use to solve such problems. This work applies data-mining techniques to quantitatively study the processes that designers use to solve configuration design problems. The guiding goal is to extract beneficial design process heuristics that are generalizable to the entire class of problems. The extraction of these human problem-solving heuristics is automated through the application of hidden Markov models to the data from two behavioral studies. Results show that designers proceed through four procedural states in solving configuration design problems, roughly transitioning from topology design to shape and …",
"author": "Christopher McComb and Jonathan Cagan and Kenneth Kotovsky",
"citation": "Journal of Mechanical Design, 2017",
"conference": null,
"journal": "Journal of Mechanical Design",
"number": null,
"pages": null,
"pub_year": 2017,
"publisher": "ASME",
"title": "Mining Process Heuristics from Designer Action Data via Hidden Markov Models",
"volume": null
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@article{alzayed2021areyoufeeling,
abstract = {Having empathy in the design process can help engineers relate to the end-user by identifying what and why certain experiences are meaningful. While there have been efforts to identify the factors that impact empathic tendencies in engineering, there has been limited evidence on how a students’ trait empathy or empathic self-efficacy develops over a design project or what factors impact this development. The current study was developed to explore the development of students’ trait empathy and empathic self-efficacy development and identify the underlying impact of the design project’s context and course instructor through a study with 103 engineering students. Students’ trait empathy and empathic self-efficacy were measured across each of the four design stages (problem formulation, concept generation, concept selection, and final conceptual design) during an 8-week project. The results highlight that …},
author = {Mohammad Alsager Alzayed and Christopher McComb and Jessica Menold and Jacquelyn Huff and Scarlett R Miller},
citation = {Journal of Mechanical Design, 2021},
journal = {Journal of Mechanical Design},
pub_year = {2021},
title = {Are you feeling me? An exploration of empathy development in engineering design education}
}
| {
"ENTRYTYPE": "article",
"ID": "alzayed2021areyoufeeling",
"abstract": "Having empathy in the design process can help engineers relate to the end-user by identifying what and why certain experiences are meaningful. While there have been efforts to identify the factors that impact empathic tendencies in engineering, there has been limited evidence on how a students’ trait empathy or empathic self-efficacy develops over a design project or what factors impact this development. The current study was developed to explore the development of students’ trait empathy and empathic self-efficacy development and identify the underlying impact of the design project’s context and course instructor through a study with 103 engineering students. Students’ trait empathy and empathic self-efficacy were measured across each of the four design stages (problem formulation, concept generation, concept selection, and final conceptual design) during an 8-week project. The results highlight that …",
"author": "Mohammad Alsager Alzayed and Christopher McComb and Jessica Menold and Jacquelyn Huff and Scarlett R Miller",
"citation": "Journal of Mechanical Design, 2021",
"conference": null,
"journal": "Journal of Mechanical Design",
"number": null,
"pages": null,
"pub_year": 2021,
"publisher": null,
"title": "Are you feeling me? An exploration of empathy development in engineering design education",
"volume": null
} | 0P9w_S0AAAAJ:08ZZubdj9fEC | 72 | /scholar?hl=en&cites=3527662701135177656 | [
"3527662701135177656"
] | https://asmedigitalcollection.asme.org/mechanicaldesign/article-abstract/143/11/112301/1087576 | /scholar?oi=bibs&hl=en&q=related:uKeOTInG9DAJ:scholar.google.com/ | [
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@article{song2020towardhybridteams:,
abstract = {Human-computer hybrid teams can meet challenges in designing complex engineered systems. However, the understanding of interaction in the hybrid teams is lacking. We review the literature and identify four key attributes to construct design research platforms that support multi-phase design, hybrid teams, multiple design scenarios, and data logging. Then, we introduce a platform for unmanned aerial vehicle (UAV) design embodying these attributes. With the platform, experiments can be conducted to study how designers and intelligent computational agents interact, support, and impact each other.},
author = {Binyang Song and Nicolas F. Soria Zurita and Guanglu Zhang and Gary Stump and Corey Balon and Simon W. Miller and Michael Yukish and Jonathan Cagan and Christopher McComb},
citation = {International Design Conference - DESIGN 2020, 2020},
conference = {International Design Conference - DESIGN 2020},
pub_year = {2020},
title = {Toward Hybrid Teams: A Platform To Understand Human-Computer Collaboration During the Design of Complex Engineered Systems}
}
| {
"ENTRYTYPE": "article",
"ID": "song2020towardhybridteams:",
"abstract": "Human-computer hybrid teams can meet challenges in designing complex engineered systems. However, the understanding of interaction in the hybrid teams is lacking. We review the literature and identify four key attributes to construct design research platforms that support multi-phase design, hybrid teams, multiple design scenarios, and data logging. Then, we introduce a platform for unmanned aerial vehicle (UAV) design embodying these attributes. With the platform, experiments can be conducted to study how designers and intelligent computational agents interact, support, and impact each other.",
"author": "Binyang Song and Nicolas F. Soria Zurita and Guanglu Zhang and Gary Stump and Corey Balon and Simon W. Miller and Michael Yukish and Jonathan Cagan and Christopher McComb",
"citation": "International Design Conference - DESIGN 2020, 2020",
"conference": "International Design Conference - DESIGN 2020",
"journal": null,
"number": null,
"pages": null,
"pub_year": 2020,
"publisher": null,
"title": "Toward Hybrid Teams: A Platform To Understand Human-Computer Collaboration During the Design of Complex Engineered Systems",
"volume": null
} | 0P9w_S0AAAAJ:bFI3QPDXJZMC | 54 | /scholar?hl=en&cites=7263844905543200920 | [
"7263844905543200920"
] | https://www.cambridge.org/core/journals/proceedings-of-the-design-society-design-conference/article/toward-hybrid-teams-a-platform-to-understand-humancomputer-collaboration-during-the-design-of-complex-engineered-systems/43F715549D49ABD1BC90579425A8CDB9 | /scholar?oi=bibs&hl=en&q=related:mPBuwnRczmQJ:scholar.google.com/ | [
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@article{song2022decodingtheagility,
abstract = {Although necessary for complex problem solving, such as engineering design, team agility is often difficult to achieve in practice. The evolution of Artificial Intelligence (AI) affords unique opportunities for supporting team problem solving. While integrating assistive AI agents into human teams has at times improved team performance, it is still unclear if, how, and why AI affects team agility. A large-scale human experiment answers these questions, revealing that, with appropriately interfaced AIs, AI-assisted human teams enjoy …},
author = {Binyang Song and Joshua T. Gyory and Guanglu Zhang and Nicolas F. Soria Zurita and Gary Stump and Jay Martin and Simon W Miller and Corey Balon and Michael Yukish and Christopher McComb and Jonathan Cagan},
citation = {Design Studies, 2022},
journal = {Design Studies},
pub_year = {2022},
title = {Decoding the Agility of Artificial Intelligence-Assisted Human Design Teams}
}
| {
"ENTRYTYPE": "article",
"ID": "song2022decodingtheagility",
"abstract": "Although necessary for complex problem solving, such as engineering design, team agility is often difficult to achieve in practice. The evolution of Artificial Intelligence (AI) affords unique opportunities for supporting team problem solving. While integrating assistive AI agents into human teams has at times improved team performance, it is still unclear if, how, and why AI affects team agility. A large-scale human experiment answers these questions, revealing that, with appropriately interfaced AIs, AI-assisted human teams enjoy …",
"author": "Binyang Song and Joshua T. Gyory and Guanglu Zhang and Nicolas F. Soria Zurita and Gary Stump and Jay Martin and Simon W Miller and Corey Balon and Michael Yukish and Christopher McComb and Jonathan Cagan",
"citation": "Design Studies, 2022",
"conference": null,
"journal": "Design Studies",
"number": null,
"pages": null,
"pub_year": 2022,
"publisher": null,
"title": "Decoding the Agility of Artificial Intelligence-Assisted Human Design Teams",
"volume": null
} | 0P9w_S0AAAAJ:UHK10RUVsp4C | 53 | /scholar?hl=en&cites=12483581656711157753 | [
"12483581656711157753"
] | https://www.sciencedirect.com/science/article/pii/S0142694X2200014X | /scholar?oi=bibs&hl=en&q=related:-dfZPGCXPq0J:scholar.google.com/ | [
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@article{raina2019transferringdesignstrategies,
abstract = {Solving any design problem involves planning and strategizing, where intermediate processes are identified and then sequenced. This is an abstract skill that designers learn over time and then use across similar problems. However, this transfer of strategies in design has not been effectively modeled or leveraged within computational agents. This note presents an approach to represent design strategies using a probabilistic model. The model provides a mechanism to generate new designs based on certain design strategies while solving configuration design task in a sequential manner. This work also demonstrates that this probabilistic representation can be used to transfer strategies from human designers to computational design agents in a way that is general and useful. This transfer-driven approach opens up the possibility of identifying high-performing behavior in human designers and using it to …},
author = {Ayush Raina and Jonathan Cagan and Christopher McComb},
citation = {Journal of Mechanical Design, 2019},
journal = {Journal of Mechanical Design},
pub_year = {2019},
title = {Transferring Design Strategies from Human to Computer and Across Design Problems}
}
| {
"ENTRYTYPE": "article",
"ID": "raina2019transferringdesignstrategies",
"abstract": "Solving any design problem involves planning and strategizing, where intermediate processes are identified and then sequenced. This is an abstract skill that designers learn over time and then use across similar problems. However, this transfer of strategies in design has not been effectively modeled or leveraged within computational agents. This note presents an approach to represent design strategies using a probabilistic model. The model provides a mechanism to generate new designs based on certain design strategies while solving configuration design task in a sequential manner. This work also demonstrates that this probabilistic representation can be used to transfer strategies from human designers to computational design agents in a way that is general and useful. This transfer-driven approach opens up the possibility of identifying high-performing behavior in human designers and using it to …",
"author": "Ayush Raina and Jonathan Cagan and Christopher McComb",
"citation": "Journal of Mechanical Design, 2019",
"conference": null,
"journal": "Journal of Mechanical Design",
"number": null,
"pages": null,
"pub_year": 2019,
"publisher": null,
"title": "Transferring Design Strategies from Human to Computer and Across Design Problems",
"volume": null
} | 0P9w_S0AAAAJ:GnPB-g6toBAC | 52 | /scholar?hl=en&cites=13705217613876381624 | [
"13705217613876381624"
] | https://asmedigitalcollection.asme.org/mechanicaldesign/article-abstract/141/11/114501/955351 | /scholar?oi=bibs&hl=en&q=related:uEeHdM62Mr4J:scholar.google.com/ | [
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-0.2497128248214... | 2025-06-15 | null | null | null | null | null | 9 | 8 | 12 | 5 | 14 | 4 |
@article{gyory2022humanversusartificial,
abstract = {Managing the design process of teams has been shown to considerably improve problem-solving behaviors and resulting final outcomes. Automating this activity presents significant opportunities in delivering interventions that dynamically adapt to the state of a team in order to reap the most impact. In this work, an artificial intelligence (AI) agent is created to manage the design process of engineering teams in real time, tracking features of teams’ actions and communications during a complex design and path-planning task in multidisciplinary teams. Teams are also placed under the guidance of human process managers for comparison. Regarding outcomes, teams perform equally as well under both types of management, with trends toward even superior performance from the AI-managed teams. The managers’ intervention strategies and team perceptions of those strategies are also explored, illuminating …},
author = {Joshua T Gyory and Nicolas F Soria Zurita and Jay Martin and Corey Balon and Christopher McComb and Kenneth Kotovsky and Jonathan Cagan},
citation = {Journal of Mechanical Design, 2022},
journal = {Journal of Mechanical Design},
pub_year = {2022},
title = {Human versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design}
}
| {
"ENTRYTYPE": "article",
"ID": "gyory2022humanversusartificial",
"abstract": "Managing the design process of teams has been shown to considerably improve problem-solving behaviors and resulting final outcomes. Automating this activity presents significant opportunities in delivering interventions that dynamically adapt to the state of a team in order to reap the most impact. In this work, an artificial intelligence (AI) agent is created to manage the design process of engineering teams in real time, tracking features of teams’ actions and communications during a complex design and path-planning task in multidisciplinary teams. Teams are also placed under the guidance of human process managers for comparison. Regarding outcomes, teams perform equally as well under both types of management, with trends toward even superior performance from the AI-managed teams. The managers’ intervention strategies and team perceptions of those strategies are also explored, illuminating …",
"author": "Joshua T Gyory and Nicolas F Soria Zurita and Jay Martin and Corey Balon and Christopher McComb and Kenneth Kotovsky and Jonathan Cagan",
"citation": "Journal of Mechanical Design, 2022",
"conference": null,
"journal": "Journal of Mechanical Design",
"number": null,
"pages": null,
"pub_year": 2022,
"publisher": null,
"title": "Human versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design",
"volume": null
} | 0P9w_S0AAAAJ:dTyEYWd-f8wC | 55 | /scholar?hl=en&cites=4215114512571339010 | [
"4215114512571339010"
] | https://asmedigitalcollection.asme.org/mechanicaldesign/article-abstract/144/2/021405/1120421 | /scholar?oi=bibs&hl=en&q=related:ApFoCVEYfzoJ:scholar.google.com/ | [
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@article{murphy2018usingautoencodedvoxel,
abstract = {Additive Manufacturing (AM) allows designers to create intricate geometries that were once too complex or expensive to achieve through traditional manufacturing processes. Currently, designing parts using features specific to AM, commonly referred to as Design for Additive Manufacturing (DfAM), is restricted to experts in the field. As a result novices in industry may overlook potentially transformational design potential enabled by AM. This project aims to automate DfAM through deep learning making it accessible to a broader audience, and enabling designers of all skill levels to leverage unique AM geometries when creating new designs. To execute such an approach, a database of files was acquired from industry-sponsored AM challenges focused on lightweight design. These files were converted to a voxelized format, which provides more robust information for machine learning applications. Next, an autoencoder was constructed to a low-dimensional representation of the part designs. Finally, that autoencoder was used to construct a deep neural network capable of predicting various DfAM attributes. This work demonstrates a novel foray towards a more extensive DfAM support system that supports designers at all experience levels.},
author = {Christian Murphy and Nicholas A Meisel and Timothy W Simpson and Christopher McComb},
citation = {29th Annual International Solid Freeform Fabrication Symposium, 2018},
conference = {29th Annual International Solid Freeform Fabrication Symposium},
pub_year = {2018},
title = {Using Autoencoded Voxel Patterns To Predict Part Mass, Required Support Material, and Build Time}
}
| {
"ENTRYTYPE": "article",
"ID": "murphy2018usingautoencodedvoxel",
"abstract": "Additive Manufacturing (AM) allows designers to create intricate geometries that were once too complex or expensive to achieve through traditional manufacturing processes. Currently, designing parts using features specific to AM, commonly referred to as Design for Additive Manufacturing (DfAM), is restricted to experts in the field. As a result novices in industry may overlook potentially transformational design potential enabled by AM. This project aims to automate DfAM through deep learning making it accessible to a broader audience, and enabling designers of all skill levels to leverage unique AM geometries when creating new designs. To execute such an approach, a database of files was acquired from industry-sponsored AM challenges focused on lightweight design. These files were converted to a voxelized format, which provides more robust information for machine learning applications. Next, an autoencoder was constructed to a low-dimensional representation of the part designs. Finally, that autoencoder was used to construct a deep neural network capable of predicting various DfAM attributes. This work demonstrates a novel foray towards a more extensive DfAM support system that supports designers at all experience levels.",
"author": "Christian Murphy and Nicholas A Meisel and Timothy W Simpson and Christopher McComb",
"citation": "29th Annual International Solid Freeform Fabrication Symposium, 2018",
"conference": "29th Annual International Solid Freeform Fabrication Symposium",
"journal": null,
"number": null,
"pages": null,
"pub_year": 2018,
"publisher": null,
"title": "Using Autoencoded Voxel Patterns To Predict Part Mass, Required Support Material, and Build Time",
"volume": null
} | 0P9w_S0AAAAJ:k_IJM867U9cC | 41 | /scholar?hl=en&cites=5897559700544115889,12210029693594976159,8090726372111810265 | [
"5897559700544115889",
"12210029693594976159",
"8090726372111810265"
] | https://repositories.lib.utexas.edu/bitstreams/1d954d13-2bce-4293-b26e-b1ed67735a24/download | /scholar?oi=bibs&hl=en&q=related:sVSXEE1X2FEJ:scholar.google.com/ | [
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-0.62555861473083... | 2025-06-15 | null | null | null | null | 3 | 5 | 7 | 7 | 9 | 6 | 4 |
@article{mccomb2016utilizingmarkovchains,
abstract = {Design often involves searching for a final design solution by iteratively modifying and adjusting a current design. Through this process designers are able to improve the quality of the current design and also learn what patterns of operations are most likely to lead to the quickest future improvements. Prior work in psychology has shown that humans can be adept at learning how to apply short sequences of operations for maximum effect while solving a problem. This work explores the sequencing of operations specifically within the domain of engineering design by examining the results of a human study in which participants designed trusses. A statistical analysis of the data from that study uses Markov Chains to show with high confidence that meaningful operation sequences exist. This work also uses an agent-based modeling framework in conjunction with Markov Chain concepts to simulate the …},
author = {Christopher McComb and Jonathan Cagan and Kenneth Kotovsky},
citation = {Eight International Conference on Design Computing and Cognition, 2016},
pub_year = {2016},
publisher = {Springer},
title = {Utilizing Markov Chains to Understand Operation Sequencing in Design Tasks}
}
| {
"ENTRYTYPE": "article",
"ID": "mccomb2016utilizingmarkovchains",
"abstract": "Design often involves searching for a final design solution by iteratively modifying and adjusting a current design. Through this process designers are able to improve the quality of the current design and also learn what patterns of operations are most likely to lead to the quickest future improvements. Prior work in psychology has shown that humans can be adept at learning how to apply short sequences of operations for maximum effect while solving a problem. This work explores the sequencing of operations specifically within the domain of engineering design by examining the results of a human study in which participants designed trusses. A statistical analysis of the data from that study uses Markov Chains to show with high confidence that meaningful operation sequences exist. This work also uses an agent-based modeling framework in conjunction with Markov Chain concepts to simulate the …",
"author": "Christopher McComb and Jonathan Cagan and Kenneth Kotovsky",
"citation": "Eight International Conference on Design Computing and Cognition, 2016",
"conference": null,
"journal": null,
"number": null,
"pages": null,
"pub_year": 2016,
"publisher": "Springer",
"title": "Utilizing Markov Chains to Understand Operation Sequencing in Design Tasks",
"volume": null
} | 0P9w_S0AAAAJ:_kc_bZDykSQC | 39 | /scholar?hl=en&cites=5410037423659347493 | [
"5410037423659347493"
] | https://link.springer.com/chapter/10.1007/978-3-319-44989-0_22 | /scholar?oi=bibs&hl=en&q=related:Je7Iil9QFEsJ:scholar.google.com/ | [
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0.207357615232467... | 2025-06-15 | null | 1 | 9 | 6 | 7 | 4 | 3 | 4 | 2 | 3 | null |
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