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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 176–192 of 192 papers

TitleStatusHype
From PINNs to PIKANs: Recent Advances in Physics-Informed Machine Learning—0
Further Exploration of Precise Binding Energies from Physics Informed Machine Learning and the Development of a Practical Ensemble Model—0
Generalizable and Fast Surrogates: Model Predictive Control of Articulated Soft Robots using Physics-Informed Neural Networks—0
Grey-box models for wave loading prediction—0
h-analysis and data-parallel physics-informed neural networks—0
How important are activation functions in regression and classification? A survey, performance comparison, and future directions—0
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
Multi-scale Digital Twin: Developing a fast and physics-informed surrogate model for groundwater contamination with uncertain climate models—0
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