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Physics-informed machine learning

Machine learning used to represent physics-based and/or engineering models

Papers

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Showing 141–150 of 192 papers

TitleStatusHype
Physics-informed machine learning for moving load problems—0
A Physics-Informed Machine Learning for Electricity Markets: A NYISO Case Study—0
Feature-adjacent multi-fidelity physics-informed machine learning for partial differential equations—0
Viscoelastic Constitutive Artificial Neural Networks (vCANNs) - a framework for data-driven anisotropic nonlinear finite viscoelasticityCode0
Discrete-Time Nonlinear Feedback Linearization via Physics-Informed Machine Learning—0
Efficient Bayesian Physics Informed Neural Networks for Inverse Problems via Ensemble Kalman Inversion—0
MetaPhysiCa: OOD Robustness in Physics-informed Machine Learning—0
h-analysis and data-parallel physics-informed neural networks—0
Neural Operator: Is data all you need to model the world? An insight into the impact of Physics Informed Machine Learning—0
L-HYDRA: Multi-Head Physics-Informed Neural NetworksCode0
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