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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 51–60 of 192 papers

TitleStatusHype
Empirical modeling and hybrid machine learning framework for nucleate pool boiling on microchannel structured surfaces—0
Enhancing predictive skills in physically-consistent way: Physics Informed Machine Learning for Hydrological Processes—0
A Physics-Informed Machine Learning Framework for Safe and Optimal Control of Autonomous Systems—0
AI Foundation Models for Weather and Climate: Applications, Design, and Implementation—0
Neural Operator: Is data all you need to model the world? An insight into the impact of Physics Informed Machine Learning—0
Feature-adjacent multi-fidelity physics-informed machine learning for partial differential equations—0
Filtered Partial Differential Equations: a robust surrogate constraint in physics-informed deep learning framework—0
Data-driven Optimal Power Flow: A Physics-Informed Machine Learning Approach—0
Data-driven AC Optimal Power Flow with Physics-informed Learning and Calibrations—0
An operator preconditioning perspective on training in physics-informed machine learning—0
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