SOTAVerified

Physics-informed machine learning

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

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 51–75 of 192 papers

TitleStatusHype
A Physics-Informed Machine Learning Approach for Solving Distributed Order Fractional Differential Equations—0
State-space models are accurate and efficient neural operators for dynamical systemsCode0
(Un)supervised Learning of Maximal Lyapunov Functions—0
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
Adaptive Training of Grid-Dependent Physics-Informed Kolmogorov-Arnold NetworksCode2
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
Differentiable Neural-Integrated Meshfree Method for Forward and Inverse Modeling of Finite Strain HyperelasticityCode0
Physics-Informed Machine Learning for Smart Additive Manufacturing—0
Deep Learning Evidence for Global Optimality of Gerver's SofaCode0
Predicting 3D Rigid Body Dynamics with Deep Residual Network—0
Physics-Informed Machine Learning Towards A Real-Time Spacecraft Thermal Simulator—0
Finite Operator Learning: Bridging Neural Operators and Numerical Methods for Efficient Parametric Solution and Optimization of PDEsCode1
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
Non-destructive Degradation Pattern Decoupling for Ultra-early Battery Prototype Verification Using Physics-informed Machine LearningCode2
Physics-Informed Machine Learning On Polar Ice: A Survey—0
Show:102550
← PrevPage 3 of 8Next →

No leaderboard results yet.