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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 51–100 of 192 papers

TitleStatusHype
Identifying Ordinary Differential Equations for Data-efficient Model-based Reinforcement Learning—0
Enhanced BPINN Training Convergence in Solving General and Multi-scale Elliptic PDEs with Noise—0
Inferring turbulent velocity and temperature fields and their statistics from Lagrangian velocity measurements using physics-informed Kolmogorov-Arnold Networks—0
KKANs: Kurkova-Kolmogorov-Arnold Networks and Their Learning Dynamics—0
Label Propagation Training Schemes for Physics-Informed Neural Networks and Gaussian Processes—0
(Un)supervised Learning of Maximal Lyapunov Functions—0
Learning ergodic averages in chaotic systems—0
Machine Learning with Physics Knowledge for Prediction: A Survey—0
MetaPhysiCa: OOD Robustness in Physics-informed Machine Learning—0
Towards Model Reduction for Power System Transients with Physics-Informed PDE—0
A physics-informed Bayesian optimization method for rapid development of electrical machines—0
Potential failures of physics-informed machine learning in traffic flow modeling: theoretical and experimental analysis—0
TS-PIELM: Time-Stepping Physics-Informed Extreme Learning Machine Facilitates Soil Consolidation Analyses—0
AutoMat: Accelerated Computational Electrochemical systems Discovery—0
A Comparative Evaluation of Additive Separability Tests for Physics-Informed Machine Learning—0
A comprehensive and FAIR comparison between MLP and KAN representations for differential equations and operator networks—0
A Critical Review of Physics-Informed Machine Learning Applications in Subsurface Energy Systems—0
A Data-driven Crowd Simulation Framework Integrating Physics-informed Machine Learning with Navigation Potential Fields—0
AdjointNet: Constraining machine learning models with physics-based codes—0
Advancing Hybrid Quantum Neural Network for Alternative Current Optimal Power Flow—0
AI Foundation Models for Weather and Climate: Applications, Design, and Implementation—0
A Mechanism-Learning Deeply Coupled Model for Remote Sensing Retrieval of Global Land Surface Temperature—0
A Multimodal Physics-Informed Neural Network Approach for Mean Radiant Temperature Modeling—0
An interpretation of the Brownian bridge as a physics-informed prior for the Poisson equation—0
An operator preconditioning perspective on training in physics-informed machine learning—0
Physics-informed machine learning for composition-process-property alloy design: shape memory alloy demonstration—0
A Physics-Informed Machine Learning Approach for Solving Heat Transfer Equation in Advanced Manufacturing and Engineering Applications—0
A Physics-Informed Machine Learning Approach for Solving Distributed Order Fractional Differential Equations—0
A Physics-informed Machine Learning-based Control Method for Nonlinear Dynamic Systems with Highly Noisy Measurements—0
A Physics-Informed Machine Learning for Electricity Markets: A NYISO Case Study—0
A Physics-Informed Machine Learning Framework for Safe and Optimal Control of Autonomous Systems—0
A Physics-Informed Machine Learning Model for Porosity Analysis in Laser Powder Bed Fusion Additive Manufacturing—0
A physics-informed machine learning model for reconstruction of dynamic loads—0
A Physics-informed machine learning model for time-dependent wave runup prediction—0
Physics-Informed Machine Learning for Smart Additive Manufacturing—0
Physics-Informed Machine Learning for the Inverse Design of Wave Scattering Clusters—0
Physics-Informed Machine Learning Models for Predicting the Progress of Reactive-Mixing—0
Physics-informed Machine Learning of Parameterized Fundamental Diagrams—0
Physics-Informed Machine Learning of Argon Gas-Driven Melt Pool Dynamics—0
Physics-informed machine learning of redox flow battery based on a two-dimensional unit cell model—0
Physics-informed machine learning of the correlation functions in bulk fluids—0
Physics-Informed Machine Learning On Polar Ice: A Survey—0
Physics Informed Machine Learning (PIML) methods for estimating the remaining useful lifetime (RUL) of aircraft engines—0
Physics-Informed Machine Learning Regulated by Finite Element Analysis for Simulation Acceleration of Laser Powder Bed Fusion—0
Physics-Informed Machine Learning Towards A Real-Time Spacecraft Thermal Simulator—0
Physics-informed Modularized Neural Network for Advanced Building Control by Deep Reinforcement Learning—0
Physics-informed neural networks for pathloss prediction—0
A Metalearning Approach for Physics-Informed Neural Networks (PINNs): Application to Parameterized PDEs—0
Residual-based physics-informed transfer learning: A hybrid method for accelerating long-term CFD simulations via deep learning—0
Scalable Neural Dynamic Equivalence for Power Systems—0
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