SOTAVerified

Physics-informed machine learning

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

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 151–192 of 192 papers

TitleStatusHype
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
Scalable algorithms for physics-informed neural and graph networks—0
Physics-informed machine learning techniques for edge plasma turbulence modelling in computational theory and experimentCode0
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
Physics informed machine learning with Smoothed Particle Hydrodynamics: Hierarchy of reduced Lagrangian models of turbulenceCode1
Multi-Objective Loss Balancing for Physics-Informed Deep LearningCode1
Physics-informed Neural Networks-based Model Predictive Control for Multi-link ManipulatorsCode1
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
Discovering nonlinear resonances through physics-informed machine learning—0
Enhancing predictive skills in physically-consistent way: Physics Informed Machine Learning for Hydrological Processes—0
NETWORK COMPRESSION FOR MACHINE-LEARNT FLUID SIMULATIONS—0
A Physics-Informed Machine Learning Model for Porosity Analysis in Laser Powder Bed Fusion Additive Manufacturing—0
Physics-Informed Machine Learning Simulator for Wildfire PropagationCode1
AutoMat: Accelerated Computational Electrochemical systems Discovery—0
A Physics-Informed Machine Learning Approach for Solving Heat Transfer Equation in Advanced Manufacturing and Engineering Applications—0
Analyzing Koopman approaches to physics-informed machine learning for long-term sea-surface temperature forecastingCode1
QRnet: optimal regulator design with LQR-augmented neural networks—0
Physics-informed machine learning for the COVID-19 pandemic: Adherence to social distancing and short-term predictions for eight countriesCode0
Physics-Informed Deep Neural Networks for Transient Electromagnetic AnalysisCode0
Universal Battery Performance and Degradation Model for Electric Aircraft—0
Physics-informed machine learning for sensor fault detection with flight test data—0
Data-driven Optimal Power Flow: A Physics-Informed Machine Learning Approach—0
A physics-informed neural network for wind turbine main bearing fatigueCode1
Physics-informed machine learning for composition-process-property alloy design: shape memory alloy demonstration—0
Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaicsCode1
Learning ergodic averages in chaotic systems—0
Tensor Basis Gaussian Process Models of Hyperelastic Materials—0
Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systemsCode1
Physics-informed neural networks for corrosion-fatigue prognosisCode1
Physics-Informed Machine Learning Models for Predicting the Progress of Reactive-Mixing—0
Fleet Prognosis with Physics-informed Recurrent Neural NetworksCode1
Prediction of Reynolds Stresses in High-Mach-Number Turbulent Boundary Layers using Physics-Informed Machine Learning—0
Show:102550
← PrevPage 4 of 4Next →

No leaderboard results yet.