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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 161–170 of 192 papers

TitleStatusHype
Physics informed machine learning with Smoothed Particle Hydrodynamics: Hierarchy of reduced Lagrangian models of turbulenceCode1
Multi-Objective Loss Balancing for Physics-Informed Deep LearningCode1
Physics-informed Neural Networks-based Model Predictive Control for Multi-link ManipulatorsCode1
AdjointNet: Constraining machine learning models with physics-based codes—0
Physics-Informed Machine Learning Method for Large-Scale Data Assimilation ProblemsCode0
Grey-box models for wave loading prediction—0
Numerical Gaussian process Kalman filtering for spatiotemporal systems—0
Applying physics-based loss functions to neural networks for improved generalizability in mechanics problems—0
Discovering nonlinear resonances through physics-informed machine learning—0
Enhancing predictive skills in physically-consistent way: Physics Informed Machine Learning for Hydrological Processes—0
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