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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 101–150 of 192 papers

TitleStatusHype
Discovering Artificial Viscosity Models for Discontinuous Galerkin Approximation of Conservation Laws using Physics-Informed Machine Learning—0
Replication Study: Enhancing Hydrological Modeling with Physics-Guided Machine Learning—0
Kolmogorov n-Widths for Multitask Physics-Informed Machine Learning (PIML) Methods: Towards Robust MetricsCode0
Physics-informed machine learning as a kernel methodCode0
A Review of Physics-Informed Machine Learning Methods with Applications to Condition Monitoring and Anomaly Detection—0
Physics-Informed Calibration of Aeromagnetic Compensation in Magnetic Navigation Systems using Liquid Time-Constant NetworksCode0
A Physics-informed machine learning model for time-dependent wave runup prediction—0
A Comparative Evaluation of Additive Separability Tests for Physics-Informed Machine Learning—0
Randomized Physics-Informed Machine Learning for Uncertainty Quantification in High-Dimensional Inverse Problems—0
Value Approximation for Two-Player General-Sum Differential Games with State ConstraintsCode0
Optimal Power Flow in Highly Renewable Power System Based on Attention Neural Networks—0
Neural-Integrated Meshfree (NIM) Method: A differentiable programming-based hybrid solver for computational mechanics—0
A Physics-informed Machine Learning-based Control Method for Nonlinear Dynamic Systems with Highly Noisy Measurements—0
Filtered Partial Differential Equations: a robust surrogate constraint in physics-informed deep learning framework—0
Zero Coordinate Shift: Whetted Automatic Differentiation for Physics-informed Operator LearningCode0
Overview of Physics-Informed Machine Learning Inversion of Geophysical Data—0
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
AI Foundation Models for Weather and Climate: Applications, Design, and Implementation—0
Physics-Informed Machine Learning for Data Anomaly Detection, Classification, Localization, and Mitigation: A Review, Challenges, and Path Forward—0
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
Feature-adjacent multi-fidelity physics-informed machine learning for partial differential equations—0
Viscoelastic Constitutive Artificial Neural Networks (vCANNs) - a framework for data-driven anisotropic nonlinear finite viscoelasticityCode0
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
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