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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 76–100 of 192 papers

TitleStatusHype
Physics-informed Discretization-independent Deep Compositional Operator NetworkCode0
Data-driven AC Optimal Power Flow with Physics-informed Learning and Calibrations—0
Label Propagation Training Schemes for Physics-Informed Neural Networks and Gaussian Processes—0
Fully-fused Multi-Layer Perceptrons on Intel Data Center GPUsCode2
Evaluation of GlassNet for physics-informed machine learning of glass stability and glass-forming abilityCode0
Physics-Informed Machine Learning for Seismic Response Prediction OF Nonlinear Steel Moment Resisting Frame Structures—0
Physics-Informed Machine Learning for the Inverse Design of Wave Scattering Clusters—0
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
Social Physics Informed Diffusion Model for Crowd SimulationCode1
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 Gaussian Process Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential EquationsCode1
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
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