Comment on “Trans-Net: A transferable pretrained neural networks based on temporal domain decomposition for solving partial differential equations” by D. Zhang, Y. Li, and S. Ying

Author:

Penwarden Michael

Publisher

Elsevier BV

Reference5 articles.

1. Trans-Net: A transferable pretrained neural networks based on temporal domain decomposition for solving partial differential equations;Zhang;Computer Physics Communications,2024

2. A unified scalable framework for causal sweeping strategies for physics-informed neural networks (PINNs) and their temporal decompositions;Penwarden;Journal of Computational Physics,2023

3. Physics-informed neural networks for the shallow-water equations on the sphere;Bihlo;Journal of Computational Physics,2022

4. Characterizing possible failure modes in physics-informed neural networks;Krishnapriyan;Advances in Neural Information Processing Systems,2021

5. Solving Allen-Cahn and Cahn-Hilliard equations using the adaptive physics informed neural networks;Zhao;Communications in Computational Physics,2021

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