arXiv Machine Learning August 6, 2026 By Xujia Chen, Xinyue Hu, Letian Chen, Yi Liu, Wenhui Fan

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

Read the original on arXiv Machine Learning →

arXiv:2608. 04778v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) incorporate governing equations into neural-network training and can approximate PDE solutions without requiring large observational datasets.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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Jun 4

On the training of physics-informed neural operators for solving parametric partial differential equations

Physics-informed neural operators (PINOs) aim to learn solution operators for partial differential equations by using the governing physics as supervision, rather than relying solely on paired input-output simulation data. By incorporating physical constraints into the training objective, PINOs combine the cross-instance generalization of neural operators with the data efficiency of physics-informed learning.

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