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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 131–140 of 192 papers

TitleStatusHype
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
Physics-constrained deep learning postprocessing of temperature and humidityCode1
Physics-informed neural networks for pathloss prediction—0
Multi-scale Digital Twin: Developing a fast and physics-informed surrogate model for groundwater contamination with uncertain climate models—0
Physics-Informed Machine Learning: A Survey on Problems, Methods and ApplicationsCode2
Physics Informed Machine Learning for Chemistry Tabulation—0
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