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arXiv Machine Learning By Shunye Wang, Haochen Wen, Shuo Li Liu, Xuanyi Wang, Lihao Liu, Zhongying Deng

Transferability of Learned States in Neural PDE Solvers

Read the original on arXiv Machine Learning →

The paper introduces a framework for evaluating how well learned states from neural PDE solvers can be reused in new contexts. By separating solution accuracy, learning contribution, and numerical utility through paired state comparisons and cost accounting, the authors audit 12 papers and conduct experiments on linear PDE families, Fourier neural operators, and convolutional networks. Their results show that matched state comparisons and downstream computational evidence can lead to significant savings in conjugate‑gradient iterations and demonstrate finite‑budget pretraining value.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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