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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 51–75 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
A Comparative Evaluation of Additive Separability Tests for Physics-Informed Machine Learning—0
Data-driven Optimal Power Flow: A Physics-Informed Machine Learning Approach—0
FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design—0
Data-driven AC Optimal Power Flow with Physics-informed Learning and Calibrations—0
From PINNs to PIKANs: Recent Advances in Physics-Informed Machine Learning—0
An operator preconditioning perspective on training in physics-informed machine learning—0
Machine Learning with Physics Knowledge for Prediction: A Survey—0
An interpretation of the Brownian bridge as a physics-informed prior for the Poisson equation—0
Calibrating constitutive models with full-field data via physics informed neural networks—0
Potential failures of physics-informed machine learning in traffic flow modeling: theoretical and experimental analysis—0
Breaking the Diffraction Barrier for Passive Sources: Parameter-Decoupled Superresolution Assisted by Physics-Informed Machine Learning—0
Inferring turbulent velocity and temperature fields and their statistics from Lagrangian velocity measurements using physics-informed Kolmogorov-Arnold Networks—0
Enhanced BPINN Training Convergence in Solving General and Multi-scale Elliptic PDEs with Noise—0
(Un)supervised Learning of Maximal Lyapunov Functions—0
Learning ergodic averages in chaotic systems—0
KKANs: Kurkova-Kolmogorov-Arnold Networks and Their Learning Dynamics—0
BridgeNet: A Hybrid, Physics-Informed Machine Learning Framework for Solving High-Dimensional Fokker-Planck Equations—0
Identifying Ordinary Differential Equations for Data-efficient Model-based Reinforcement Learning—0
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