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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
Physics-informed machine learning for moving load problems—0
A Physics-Informed Machine Learning for Electricity Markets: A NYISO Case Study—0
Viscoelastic Constitutive Artificial Neural Networks (vCANNs) - a framework for data-driven anisotropic nonlinear finite viscoelasticityCode0
Feature-adjacent multi-fidelity physics-informed machine learning for partial differential equations—0
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
Physics-constrained deep learning postprocessing of temperature and humidityCode1
Physics-informed neural networks for pathloss prediction—0
Multi-scale Digital Twin: Developing a fast and physics-informed surrogate model for groundwater contamination with uncertain climate models—0
Physics-Informed Machine Learning: A Survey on Problems, Methods and ApplicationsCode2
Physics Informed Machine Learning for Chemistry Tabulation—0
Valuation of Public Bus Electrification with Open Data—0
Physics-Informed Machine Learning of Dynamical Systems for Efficient Bayesian InferenceCode1
How important are activation functions in regression and classification? A survey, performance comparison, and future directions—0
STDEN: Towards Physics-Guided Neural Networks for Traffic Flow PredictionCode1
Physics-informed Machine Learning of Parameterized Fundamental Diagrams—0
Neural modal ordinary differential equations: Integrating physics-based modeling with neural ordinary differential equations for modeling high-dimensional monitored structuresCode0
Physics-informed machine learning for Structural Health Monitoring—0
Noise-aware Physics-informed Machine Learning for Robust PDE DiscoveryCode0
Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure managementCode1
Residual-based physics-informed transfer learning: A hybrid method for accelerating long-term CFD simulations via deep learning—0
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