Low-Rank Approximation for Multiscale PDEs
نویسندگان
چکیده
Historically, analysis for multiscale PDEs is largely unified while numerical schemes tend to be equation-specific. In this paper, we propose a framework computing problems through random sampling. This achieved by incorporating randomized SVD solvers and manifold learning techniques numerically reconstruct the low-rank features of PDEs. We use radiative transfer equation elliptic with rough media showcase application framework.
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ژورنال
عنوان ژورنال: Notices of the American Mathematical Society
سال: 2022
ISSN: ['0002-9920', '1088-9477']
DOI: https://doi.org/10.1090/noti2488