Port-Hamiltonian neural networks extended to PDEs recover the Hamiltonian and dissipation of nonlinear string dynamics from data and outperform non-physics-informed baselines.
Physics-Enhanced Machine Learning: a position paper for dynamical systems investigations , volume=
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A pipeline that uses SysML diagrams enhanced by NLP and LLMs to automatically generate dynamical system computational models from unstructured text, demonstrated on a simple pendulum with better results than zero-shot LLMs.
Aerodynamic pressure signals enable real-time, interpretable detection and severity classification of structural damage in elastic beam-like structures via CNN enhanced with physics insights and XAI.
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Identifying the nonlinear string dynamics with port-Hamiltonian neural networks
Port-Hamiltonian neural networks extended to PDEs recover the Hamiltonian and dissipation of nonlinear string dynamics from data and outperform non-physics-informed baselines.
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Text to model via SysML: Automated generation of dynamical system computational models from unstructured natural language text via enhanced System Modeling Language diagrams
A pipeline that uses SysML diagrams enhanced by NLP and LLMs to automatically generate dynamical system computational models from unstructured text, demonstrated on a simple pendulum with better results than zero-shot LLMs.
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Towards Interpretable Damage Detection based on Aerodynamic Pressure Measurements
Aerodynamic pressure signals enable real-time, interpretable detection and severity classification of structural damage in elastic beam-like structures via CNN enhanced with physics insights and XAI.