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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 151–175 of 192 papers

TitleStatusHype
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 for Chemistry Tabulation—0
Valuation of Public Bus Electrification with Open Data—0
How important are activation functions in regression and classification? A survey, performance comparison, and future directions—0
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
Residual-based physics-informed transfer learning: A hybrid method for accelerating long-term CFD simulations via deep learning—0
Grid-SiPhyR: An end-to-end learning to optimize framework for combinatorial problems in power systems—0
Towards Size-Independent Generalization Bounds for Deep Operator NetsCode0
Physics-informed machine learning techniques for edge plasma turbulence modelling in computational theory and experimentCode0
Scalable algorithms for physics-informed neural and graph networks—0
When Physics Meets Machine Learning: A Survey of Physics-Informed Machine Learning—0
Calibrating constitutive models with full-field data via physics informed neural networks—0
Physics-informed ConvNet: Learning Physical Field from a Shallow Neural Network—0
Numerical Approximation in CFD Problems Using Physics Informed Machine Learning—0
Towards Model Reduction for Power System Transients with Physics-Informed PDE—0
A Metalearning Approach for Physics-Informed Neural Networks (PINNs): Application to Parameterized PDEs—0
AdjointNet: Constraining machine learning models with physics-based codes—0
Physics-Informed Machine Learning Method for Large-Scale Data Assimilation ProblemsCode0
Grey-box models for wave loading prediction—0
Numerical Gaussian process Kalman filtering for spatiotemporal systems—0
Applying physics-based loss functions to neural networks for improved generalizability in mechanics problems—0
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