SOTAVerified

Physics-informed machine learning

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

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 101–150 of 192 papers

TitleStatusHype
An operator preconditioning perspective on training in physics-informed machine learning—0
Physics-Informed Induction Machine Modelling—0
Scalable Neural Dynamic Equivalence for Power Systems—0
Physics-Informed Machine Learning for Data Anomaly Detection, Classification, Localization, and Mitigation: A Review, Challenges, and Path Forward—0
AI Foundation Models for Weather and Climate: Applications, Design, and Implementation—0
Physics-informed neural networks for highly compressible flowsCode1
Slow Invariant Manifolds of Singularly Perturbed Systems via Physics-Informed Machine Learning—0
Estimating irregular water demands with physics-informed machine learning to inform leakage detectionCode0
Separable Hamiltonian Neural NetworksCode0
Physics-informed machine learning of the correlation functions in bulk fluids—0
Hyperspectral Blind Unmixing using a Double Deep Image PriorCode0
Neural oscillators for generalization of physics-informed machine learningCode0
A physics-informed machine learning model for reconstruction of dynamic loads—0
A Critical Review of Physics-Informed Machine Learning Applications in Subsurface Energy Systems—0
Physics-informed Gaussian process model for Euler-Bernoulli beam elements—0
Physics-Informed Machine Learning of Argon Gas-Driven Melt Pool Dynamics—0
Physics-informed Machine Learning for Calibrating Macroscopic Traffic Flow Models—0
Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems—0
A Machine Learning Pressure Emulator for Hydrogen EmbrittlementCode0
An analysis of Universal Differential Equations for data-driven discovery of Ordinary Differential EquationsCode0
Physics-informed machine learning of redox flow battery based on a two-dimensional unit cell model—0
Physics-Informed Computer Vision: A Review and Perspectives—0
Nearly Optimal VC-Dimension and Pseudo-Dimension Bounds for Deep Neural Network Derivatives—0
Unsupervised Discovery of Extreme Weather Events Using Universal Representations of Emergent OrganizationCode0
Π-ML: A dimensional analysis-based machine learning parameterization of optical turbulence in the atmospheric surface layerCode0
Physics-informed machine learning for moving load problems—0
A Physics-Informed Machine Learning for Electricity Markets: A NYISO Case Study—0
Viscoelastic Constitutive Artificial Neural Networks (vCANNs) - a framework for data-driven anisotropic nonlinear finite viscoelasticityCode0
Feature-adjacent multi-fidelity physics-informed machine learning for partial differential equations—0
Discrete-Time Nonlinear Feedback Linearization via Physics-Informed Machine Learning—0
Efficient Bayesian Physics Informed Neural Networks for Inverse Problems via Ensemble Kalman Inversion—0
MetaPhysiCa: OOD Robustness in Physics-informed Machine Learning—0
h-analysis and data-parallel physics-informed neural networks—0
Neural Operator: Is data all you need to model the world? An insight into the impact of Physics Informed Machine Learning—0
L-HYDRA: Multi-Head Physics-Informed Neural NetworksCode0
Physics-constrained deep learning postprocessing of temperature and humidityCode1
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: A Survey on Problems, Methods and ApplicationsCode2
Physics Informed Machine Learning for Chemistry Tabulation—0
Valuation of Public Bus Electrification with Open Data—0
Physics-Informed Machine Learning of Dynamical Systems for Efficient Bayesian InferenceCode1
How important are activation functions in regression and classification? A survey, performance comparison, and future directions—0
STDEN: Towards Physics-Guided Neural Networks for Traffic Flow PredictionCode1
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
Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure managementCode1
Residual-based physics-informed transfer learning: A hybrid method for accelerating long-term CFD simulations via deep learning—0
Show:102550
← PrevPage 3 of 4Next →

No leaderboard results yet.