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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 81–90 of 192 papers

TitleStatusHype
Machine Learning with Physics Knowledge for Prediction: A Survey—0
MetaPhysiCa: OOD Robustness in Physics-informed Machine Learning—0
Towards Model Reduction for Power System Transients with Physics-Informed PDE—0
Beyond Accuracy: EcoL2 Metric for Sustainable Neural PDE Solvers—0
Multi-scale Digital Twin: Developing a fast and physics-informed surrogate model for groundwater contamination with uncertain climate models—0
Nearly Optimal VC-Dimension and Pseudo-Dimension Bounds for Deep Neural Network Derivatives—0
AutoMat: Accelerated Computational Electrochemical systems Discovery—0
How important are activation functions in regression and classification? A survey, performance comparison, and future directions—0
Neural-Integrated Meshfree (NIM) Method: A differentiable programming-based hybrid solver for computational mechanics—0
h-analysis and data-parallel physics-informed neural networks—0
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