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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 101–125 of 192 papers

TitleStatusHype
An operator preconditioning perspective on training in physics-informed machine learning—0
Physics-Informed Induction Machine Modelling—0
Scalable Neural Dynamic Equivalence for Power Systems—0
Physics-Informed Machine Learning for Data Anomaly Detection, Classification, Localization, and Mitigation: A Review, Challenges, and Path Forward—0
AI Foundation Models for Weather and Climate: Applications, Design, and Implementation—0
Physics-informed neural networks for highly compressible flowsCode1
Slow Invariant Manifolds of Singularly Perturbed Systems via Physics-Informed Machine Learning—0
Estimating irregular water demands with physics-informed machine learning to inform leakage detectionCode0
Separable Hamiltonian Neural NetworksCode0
Physics-informed machine learning of the correlation functions in bulk fluids—0
Hyperspectral Blind Unmixing using a Double Deep Image PriorCode0
Neural oscillators for generalization of physics-informed machine learningCode0
A physics-informed machine learning model for reconstruction of dynamic loads—0
A Critical Review of Physics-Informed Machine Learning Applications in Subsurface Energy Systems—0
Physics-informed Gaussian process model for Euler-Bernoulli beam elements—0
Physics-Informed Machine Learning of Argon Gas-Driven Melt Pool Dynamics—0
Physics-informed Machine Learning for Calibrating Macroscopic Traffic Flow Models—0
Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems—0
A Machine Learning Pressure Emulator for Hydrogen EmbrittlementCode0
An analysis of Universal Differential Equations for data-driven discovery of Ordinary Differential EquationsCode0
Physics-informed machine learning of redox flow battery based on a two-dimensional unit cell model—0
Physics-Informed Computer Vision: A Review and Perspectives—0
Nearly Optimal VC-Dimension and Pseudo-Dimension Bounds for Deep Neural Network Derivatives—0
Unsupervised Discovery of Extreme Weather Events Using Universal Representations of Emergent OrganizationCode0
Π-ML: A dimensional analysis-based machine learning parameterization of optical turbulence in the atmospheric surface layerCode0
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