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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
Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaicsCode1
Physics-informed Neural Networks-based Model Predictive Control for Multi-link ManipulatorsCode1
Physics-Informed Machine Learning of Dynamical Systems for Efficient Bayesian InferenceCode1
QCPINN: Quantum-Classical Physics-Informed Neural Networks for Solving PDEsCode1
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
Kolmogorov n-Widths for Multitask Physics-Informed Machine Learning (PIML) Methods: Towards Robust MetricsCode0
Hyperspectral Blind Unmixing using a Double Deep Image PriorCode0
LaPON: A Lagrange's-mean-value-theorem-inspired operator network for solving PDEs and its application on NSECode0
L-HYDRA: Multi-Head Physics-Informed Neural NetworksCode0
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