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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 71–80 of 192 papers

TitleStatusHype
Neural Operator: Is data all you need to model the world? An insight into the impact of Physics Informed Machine Learning—0
A Physics-Informed Machine Learning Model for Porosity Analysis in Laser Powder Bed Fusion Additive Manufacturing—0
A Critical Review of Physics-Informed Machine Learning Applications in Subsurface Energy Systems—0
A Physics-Informed Machine Learning Framework for Safe and Optimal Control of Autonomous Systems—0
Enhancing predictive skills in physically-consistent way: Physics Informed Machine Learning for Hydrological Processes—0
Empirical modeling and hybrid machine learning framework for nucleate pool boiling on microchannel structured surfaces—0
A Physics-Informed Machine Learning for Electricity Markets: A NYISO Case Study—0
AI Foundation Models for Weather and Climate: Applications, Design, and Implementation—0
Efficient Bayesian Physics Informed Neural Networks for Inverse Problems via Ensemble Kalman Inversion—0
A Physics-informed Machine Learning-based Control Method for Nonlinear Dynamic Systems with Highly Noisy Measurements—0
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