SOTAVerified

Physics-informed machine learning

Machine learning used to represent physics-based and/or engineering models

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 176–192 of 192 papers

TitleStatusHype
Physics-Informed Deep Neural Networks for Transient Electromagnetic AnalysisCode0
Zero Coordinate Shift: Whetted Automatic Differentiation for Physics-informed Operator LearningCode0
DSSRNN: Decomposition-Enhanced State-Space Recurrent Neural Network for Time-Series AnalysisCode0
Physics-informed kernel learningCode0
Physics-informed machine learning as a kernel methodCode0
Evaluation of GlassNet for physics-informed machine learning of glass stability and glass-forming abilityCode0
Value Approximation for Two-Player General-Sum Differential Games with State ConstraintsCode0
State-space models are accurate and efficient neural operators for dynamical systemsCode0
An analysis of Universal Differential Equations for data-driven discovery of Ordinary Differential EquationsCode0
Adapting Physics-Informed Neural Networks to Improve ODE Optimization in Mosquito Population DynamicsCode0
Differentiable Neural-Integrated Meshfree Method for Forward and Inverse Modeling of Finite Strain HyperelasticityCode0
Physics-informed machine learning techniques for edge plasma turbulence modelling in computational theory and experimentCode0
Separable Hamiltonian Neural NetworksCode0
Deep Learning Evidence for Global Optimality of Gerver's SofaCode0
Physics-informed Discretization-independent Deep Compositional Operator NetworkCode0
Reconstructing Physics-Informed Machine Learning for Traffic Flow Modeling: a Multi-Gradient Descent and Pareto Learning ApproachCode0
Towards Size-Independent Generalization Bounds for Deep Operator NetsCode0
Show:102550
← PrevPage 8 of 8Next →

No leaderboard results yet.