5 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…
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…
Physics-informed Multiple-Input Operators for efficient dynamic response prediction of structures
Bilal Ahmed, Yuqing Qiu, Diab W. Abueidda +3
Finite element (FE) modeling is essential for structural analysis but remains computationally intensive, especially under dynamic loading. While operator learning models have shown…
Damage identification for bridges using machine learning: Development and application to KW51 bridge
Yuqing Qiu, Bilal Ahmed, Diab W. Abueidda +6
The available tools for damage identification in civil engineering structures are known to be computationally expensive and data-demanding. This paper proposes a comprehensive mach…
Physics-informed DeepONet with stiffness-based loss functions for structural response prediction
Bilal Ahmed, Yuqing Qiu, Diab W. Abueidda +4
Finite element modeling is a well-established tool for structural analysis, yet modeling complex structures often requires extensive pre-processing, significant analysis effort, an…