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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 26–50 of 192 papers

TitleStatusHype
A physics-informed machine learning model for reconstruction of dynamic loads—0
A Physics-Informed Machine Learning Model for Porosity Analysis in Laser Powder Bed Fusion Additive Manufacturing—0
A Critical Review of Physics-Informed Machine Learning Applications in Subsurface Energy Systems—0
A Physics-informed machine learning model for time-dependent wave runup prediction—0
TS-PIELM: Time-Stepping Physics-Informed Extreme Learning Machine Facilitates Soil Consolidation Analyses—0
A Physics-Informed Machine Learning Framework for Safe and Optimal Control of Autonomous Systems—0
A Physics-Informed Machine Learning for Electricity Markets: A NYISO Case Study—0
AI Foundation Models for Weather and Climate: Applications, Design, and Implementation—0
A Physics-informed Machine Learning-based Control Method for Nonlinear Dynamic Systems with Highly Noisy Measurements—0
Discrete-Time Nonlinear Feedback Linearization via Physics-Informed Machine Learning—0
Advancing Hybrid Quantum Neural Network for Alternative Current Optimal Power Flow—0
A comprehensive and FAIR comparison between MLP and KAN representations for differential equations and operator networks—0
FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design—0
A Physics-Informed Machine Learning Approach for Solving Heat Transfer Equation in Advanced Manufacturing and Engineering Applications—0
Physics-informed machine learning for composition-process-property alloy design: shape memory alloy demonstration—0
Neural Operator: Is data all you need to model the world? An insight into the impact of Physics Informed Machine Learning—0
Feature-adjacent multi-fidelity physics-informed machine learning for partial differential equations—0
DeepONet for Solving Nonlinear Partial Differential Equations with Physics-Informed Training—0
AdjointNet: Constraining machine learning models with physics-based codes—0
Discovering Artificial Viscosity Models for Discontinuous Galerkin Approximation of Conservation Laws using Physics-Informed Machine Learning—0
Discovering nonlinear resonances through physics-informed machine learning—0
A Data-driven Crowd Simulation Framework Integrating Physics-informed Machine Learning with Navigation Potential Fields—0
A Physics-Informed Machine Learning Approach for Solving Distributed Order Fractional Differential Equations—0
Efficient Bayesian Physics Informed Neural Networks for Inverse Problems via Ensemble Kalman Inversion—0
A Comparative Evaluation of Additive Separability Tests for Physics-Informed Machine Learning—0
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