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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 181–190 of 192 papers

TitleStatusHype
Physics-informed machine learning for sensor fault detection with flight test data—0
Data-driven Optimal Power Flow: A Physics-Informed Machine Learning Approach—0
A physics-informed neural network for wind turbine main bearing fatigueCode1
Physics-informed machine learning for composition-process-property alloy design: shape memory alloy demonstration—0
Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaicsCode1
Learning ergodic averages in chaotic systems—0
Tensor Basis Gaussian Process Models of Hyperelastic Materials—0
Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systemsCode1
Physics-informed neural networks for corrosion-fatigue prognosisCode1
Physics-Informed Machine Learning Models for Predicting the Progress of Reactive-Mixing—0
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