Space-time reduced order model for large-scale linear dynamical systems with application to Boltzmann transport problems

2019/10/02 by Youngsoo Choi, Peter Brown, Choi, Youngsoo +5 · 13 citations
Computer Science · Engineering · Mathematics · Physics and Astronomy · #FOS: Mathematics #Model Reduction and Neural Networks #Numerical Analysis (math.NA) #Numerical methods for differential equations #Particle accelerators and beam dynamics #cs.NA #math.NA

paper · pdf · doi:10.48550/arxiv.1910.01260

19 pages, 4 figures

openalex publication_date 2019/10/02 · arxiv created 2019/10/03 · arxiv updated 2019/10/04 · openalex created_date 2022/07/24 · openalex updated_date 2026/07/28

Abstract

A classical reduced order model for dynamical problems involves spatial reduction of the problem size. However, temporal reduction accompanied by the spatial reduction can further reduce the problem size without losing accuracy much, which results in a considerably more speed-up than the spatial reduction only. Recently, a novel space-time reduced order model for dynamical problems has been developed, where the space-time reduced order model shows an order of a hundred speed-up with a relative error of less than 0.1% for small academic problems. However, in order for the method to be applicable to a large-scale problem, an efficient space-time reduced basis construction algorithm needs to be developed. We present incremental space-time reduced basis construction algorithm. The incremental algorithm is fully parallel and scalable. Additionally, the block structure in the space-time reduced basis is exploited, which enables the avoidance of constructing the reduced space-time basis. These novel techniques are applied to a large-scale particle transport simulation with million and billion degrees of freedom. The numerical example shows that the algorithm is scalable and practical. Also, it achieves a tremendous speed-up, maintaining a good accuracy. Finally, error bounds for space-only and space-time reduced order models are derived.

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