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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 126–150 of 192 papers

TitleStatusHype
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
Physics-informed machine learning for moving load problems—0
A Physics-Informed Machine Learning for Electricity Markets: A NYISO Case Study—0
Feature-adjacent multi-fidelity physics-informed machine learning for partial differential equations—0
Viscoelastic Constitutive Artificial Neural Networks (vCANNs) - a framework for data-driven anisotropic nonlinear finite viscoelasticityCode0
Discrete-Time Nonlinear Feedback Linearization via Physics-Informed Machine Learning—0
Efficient Bayesian Physics Informed Neural Networks for Inverse Problems via Ensemble Kalman Inversion—0
MetaPhysiCa: OOD Robustness in Physics-informed Machine Learning—0
h-analysis and data-parallel physics-informed neural networks—0
Neural Operator: Is data all you need to model the world? An insight into the impact of Physics Informed Machine Learning—0
L-HYDRA: Multi-Head Physics-Informed Neural NetworksCode0
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