BriefGPT.xyz
Sep, 2023
通过泰勒展开和稀疏分解在值域和傅里叶域之间高效学习偏微分方程
Efficient Learning of PDEs via Taylor Expansion and Sparse Decomposition into Value and Fourier Domains
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Md Nasim, Yexiang Xue
TL;DR
通过利用随机投影,Reel通过在值域和频域将密集更新分解为稀疏的更新,从而加速偏微分方程的学习,并且具有更广泛的适应性。
Abstract
Accelerating the learning of
partial differential equations
(PDEs) from experimental data will speed up the pace of scientific discovery. Previous randomized algorithms exploit sparsity in PDE updates for
acceleration
→