most citedOn the efficiency of an autonomous dynamic Szilard engine operating on a single active particle

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cs.LG2026

Continual-Learning Physics-Informed Neural Networks for Parameterized Partial Differential Equations

Xujia Chen, Xinyue Hu, Letian Chen +2

Physics-informed neural networks (PINNs) incorporate governing equations into neural-network training and can approximate PDE solutions without requiring large observational datase…

cs.LG2026

Curriculum Learning of Physics-Informed Neural Networks based on Spatial Correlation

Xujia Chen, Xinyue Hu, Letian Chen +2

Physics-Informed Neural Networks (PINNs) combine deep learning with physical constraints for solving partial differential equations (PDEs), and are widely applied in fluid mechanic…