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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 76–100 of 192 papers

TitleStatusHype
Spectrally Informed Learning of Fluid Flows—0
Machine Learning with Physics Knowledge for Prediction: A Survey—0
Enhanced BPINN Training Convergence in Solving General and Multi-scale Elliptic PDEs with Noise—0
Physics-Informed Machine Learning for Grade Prediction in Froth Flotation—0
Self-tuning moving horizon estimation of nonlinear systems via physics-informed machine learning Koopman modeling—0
Inferring turbulent velocity and temperature fields and their statistics from Lagrangian velocity measurements using physics-informed Kolmogorov-Arnold Networks—0
Separable DeepONet: Breaking the Curse of Dimensionality in Physics-Informed Machine LearningCode0
Physical Data Embedding for Memory Efficient AI—0
Deep Learning Evidence for Global Optimality of Gerver's SofaCode0
Differentiable Neural-Integrated Meshfree Method for Forward and Inverse Modeling of Finite Strain HyperelasticityCode0
Physics-Informed Machine Learning for Smart Additive Manufacturing—0
Predicting 3D Rigid Body Dynamics with Deep Residual Network—0
Physics-Informed Machine Learning Towards A Real-Time Spacecraft Thermal Simulator—0
Identifying Ordinary Differential Equations for Data-efficient Model-based Reinforcement Learning—0
Physics Informed Machine Learning (PIML) methods for estimating the remaining useful lifetime (RUL) of aircraft engines—0
Differentiable Predictive Control for Large-Scale Urban Road NetworksCode0
Adapting Physics-Informed Neural Networks to Improve ODE Optimization in Mosquito Population DynamicsCode0
A comprehensive and FAIR comparison between MLP and KAN representations for differential equations and operator networks—0
Physics-Informed Machine Learning On Polar Ice: A Survey—0
Physics-informed Discretization-independent Deep Compositional Operator NetworkCode0
Data-driven AC Optimal Power Flow with Physics-informed Learning and Calibrations—0
Label Propagation Training Schemes for Physics-Informed Neural Networks and Gaussian Processes—0
Evaluation of GlassNet for physics-informed machine learning of glass stability and glass-forming abilityCode0
Physics-Informed Machine Learning for Seismic Response Prediction OF Nonlinear Steel Moment Resisting Frame Structures—0
Physics-Informed Machine Learning for the Inverse Design of Wave Scattering Clusters—0
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