SNS: A Solution-based Nonlinear Subspace method for time-dependent model order reduction

Choi, Young-Soo; Coombs, Deshawn; Anderson, Robert H.

doi:10.48550/arxiv.1809.04064

Cited by 1 publication

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“…where Âk : There is a hyper-reduction technique available to reduce the construction cost of reduced operators although we do not apply it to our numerical examples. For example, see [18,26,20] for various hyper-reduction techniques.…”

Section: Projection-based Rommentioning

confidence: 99%

Accelerating design optimization using reduced order models

Choi,

Oxberry,

White

et al. 2019

Preprint

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Although design optimization has shown its great power of automatizing the whole design process and providing an optimal design, using sophisticated computational models, its process can be formidable due to a computationally expensive large-scale linear system of equations to solve, associated with underlying physics models. We introduce a general reduced order model-based design optimization acceleration approach that is applicable not only to design optimization problems, but also to any PDE-constrained optimization problems. The acceleration is achieved by two techniques: i) allowing an inexact linear solve and ii) reducing the number of iterations in Krylov subspace iterative methods. The choice between two techniques are made, based on how close a current design point to an optimal point. The advantage of the acceleration approach is demonstrated in topology optimization examples, including both compliance minimization and stress-constrained problems, where it achieves a tremendous reduction and speed-up when a traditional preconditioner fails to achieve a considerable reduction in the number of linear solve iterations.

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Section: Projection-based Rommentioning

confidence: 99%