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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 21–30 of 192 papers

TitleStatusHype
Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure managementCode1
Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaicsCode1
Fleet Prognosis with Physics-informed Recurrent Neural NetworksCode1
Physics-informed neural networks for highly compressible flowsCode1
Neural modal ordinary differential equations: Integrating physics-based modeling with neural ordinary differential equations for modeling high-dimensional monitored structuresCode0
A Machine Learning Pressure Emulator for Hydrogen EmbrittlementCode0
Neural oscillators for generalization of physics-informed machine learningCode0
LaPON: A Lagrange's-mean-value-theorem-inspired operator network for solving PDEs and its application on NSECode0
Kolmogorov n-Widths for Multitask Physics-Informed Machine Learning (PIML) Methods: Towards Robust MetricsCode0
L-HYDRA: Multi-Head Physics-Informed Neural NetworksCode0
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