Learning Elliptic Partial Differential Equations with Randomized Linear Algebra

نویسندگان

چکیده

Given input-output pairs of an elliptic partial differential equation (PDE) in three dimensions, we derive the first theoretically-rigorous scheme for learning associated Green's function $G$. By exploiting hierarchical low-rank structure $G$, show that one can construct approximant to $G$ converges almost surely and achieves a relative error $\mathcal{O}(\Gamma_\epsilon^{-1/2}\log^3(1/\epsilon)\epsilon)$ using at most $\mathcal{O}(\epsilon^{-6}\log^4(1/\epsilon))$ training with high probability, any $0<\epsilon<1$. The quantity $0<\Gamma_\epsilon\leq 1$ characterizes quality dataset. Along way, extend randomized singular value decomposition algorithm matrices Hilbert--Schmidt operators characterize covariance kernels PDE learning.

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ژورنال

عنوان ژورنال: Foundations of Computational Mathematics

سال: 2022

ISSN: ['1615-3383', '1615-3375']

DOI: https://doi.org/10.1007/s10208-022-09556-w