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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 176–192 of 192 papers

TitleStatusHype
Discovering nonlinear resonances through physics-informed machine learning—0
Enhancing predictive skills in physically-consistent way: Physics Informed Machine Learning for Hydrological Processes—0
NETWORK COMPRESSION FOR MACHINE-LEARNT FLUID SIMULATIONS—0
A Physics-Informed Machine Learning Model for Porosity Analysis in Laser Powder Bed Fusion Additive Manufacturing—0
AutoMat: Accelerated Computational Electrochemical systems Discovery—0
A Physics-Informed Machine Learning Approach for Solving Heat Transfer Equation in Advanced Manufacturing and Engineering Applications—0
QRnet: optimal regulator design with LQR-augmented neural networks—0
Physics-informed machine learning for the COVID-19 pandemic: Adherence to social distancing and short-term predictions for eight countriesCode0
Physics-Informed Deep Neural Networks for Transient Electromagnetic AnalysisCode0
Universal Battery Performance and Degradation Model for Electric Aircraft—0
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
Physics-informed machine learning for composition-process-property alloy design: shape memory alloy demonstration—0
Learning ergodic averages in chaotic systems—0
Tensor Basis Gaussian Process Models of Hyperelastic Materials—0
Physics-Informed Machine Learning Models for Predicting the Progress of Reactive-Mixing—0
Prediction of Reynolds Stresses in High-Mach-Number Turbulent Boundary Layers using Physics-Informed Machine Learning—0
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