15 papers
Physics-Audited Agentic Discovery in Scientific Machine Learning
Diab W. Abueidda, Bilal Ahmed, Panos Pantidis +1
In agentic scientific machine learning (SciML), large language model (LLM) agents can discover surrogate models and select one by an automated score, typically an error metric. A l…
Agentic Physical AI toward a Domain-Specific Foundation Model for Energy Systems: A Case Study on Nuclear Reactor Control
Yoon Pyo Lee, Samrendra Roy, Kazuma Kobayashi +5
The prevailing paradigm in AI for physical systems: scaling general-purpose foundation models toward universal multimodal reasoning, confronts a barrier at the control interface. F…
A hybrid IFENN solver for generalizable modeling of phase-field fracture initiation and propagation
Panos Pantidis, Fouad Amin, Diab Abueidda +1
In this paper we demonstrate how the Integrated Finite Element Neural Network (IFENN) framework can effectively model the entire evolution of phase-field fracture, including the in…
Adaptive Distance-Aware Trunk Deep Operator Learning for Long-Span Roadway Bridges
Bilal Ahmed, Diab W. Abueidda, Waleed El-Sekelly +2
Long-span roadway bridges exhibit highly localized structural responses under vehicular loading, making repeated FE analysis computationally expensive for applications such as infl…
Single vs. Multiple Branches in DeepONet and S-DeepONet: Network Architecture Follows Coupling in Multiphysics Systems
Jaewan Park, Kazuma Kobayashi, Qibang Liu +3
`Real-time prediction of complex physical systems requires surrogate models that learn from data while representing strong multiphysics coupling. Deep Operator Networks have shown…
Geometry-Informed Neural Operator Transformer
Qibang Liu, Weiheng Zhong, Hadi Meidani +3
Machine-learning-based surrogate models offer significant computational efficiency and faster simulations compared to traditional numerical methods, especially for problems requiri…