TANG-Research-Group/PyroBot_FoundationModel
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AI & ML interests
The central repository for our interdisciplinary research. Here we publish our physics-informed AI models, network weights, and datasets for mechanics and resources engineering.
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Our group operates at the dynamic intersection of Artificial Intelligence, Machine Learning, and Mechanics within Energy and Resources Engineering. We are dedicated to revolutionizing traditional research paradigms by leveraging data-driven and physics-informed AI to solve frontier interdisciplinary challenges across the energy-environment-agriculture nexus.
Core Focus Areas:
- AI-Driven Cross-Scale Modeling: Developing machine learning interatomic potentials and multi-scale computational frameworks to bridge the fundamental gap between atomistic mechanics and macroscopic system performance.
- Intelligent Energy & Resource Engineering: Applying predictive ML and neural networks to optimize complex thermodynamic processes, precision thermochemical conversions, and the advanced design of sustainable materials.
- AI for Environmental & Agricultural Sustainability: Innovating programmable, responsive intelligent agents for targeted nutrient delivery and resource recovery, accelerating the transition toward precision, sustainable agriculture.
- Paradigm-Shifting Nexus Frameworks: Integrating automated machine learning with dynamic life-cycle assessment (LCA) and techno-economic analysis (TEA) to fundamentally transform how complex interdisciplinary systems are designed, evaluated, and deployed.
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