Randomized Low-Rank Runge–Kutta Methods

Authors

Hei Yin Lam
Gianluca Ceruti
Daniel Kreßner (ORCID: https://orcid.org/0000-0003-3369-2958)

Institutions

Universität Innsbruck (AT)
École Polytechnique Fédérale de Lausanne (CH)

Publication Details

Journal: SIAM Journal on Matrix Analysis and Applications
Published: 2025-06-16
DOI: https://doi.org/10.1137/24m1692691
Citations: 3
Primary Topic: Model Reduction and Neural Networks
Type: article
Field-Weighted Citation Impact: 3.97

Funders

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

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article

Randomized Low-Rank Runge–Kutta Methods

Hei Yin Lam, Gianluca Ceruti, Daniel Kreßner

Jun 16, 2025

3 citations

SIAM Journal on Matrix Analysis and Applications

Model Reduction and Neural Networks

3.97

article

Randomized Low-Rank Runge–Kutta Methods

Hei Yin Lam, Gianluca Ceruti, Daniel Kreßner

June 16, 2025article en

3 citations

Abstract

No abstract available for this paper.

SIAM Journal on Matrix Analysis and ApplicationsVol. 46(2)

Universität Innsbruck (AT), École Polytechnique Fédérale de Lausanne (CH)

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

Openalex Percentile: Top 7%

Model Reduction and Neural Networks

3.97

DOI Open Access PDF OpenAlex

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Randomized Low-Rank Runge–Kutta Methods — Hei Yin Lam, Gianluca Ceruti, et al. · SIAM Journal on Matrix Analysis and Applications (2025) | TGRS Research Map | TGRS