{ "id": "1909.11320", "version": "v1", "published": "2019-09-25T07:46:32.000Z", "updated": "2019-09-25T07:46:32.000Z", "title": "Accelerating design optimization using reduced order models", "authors": [ "Youngsoo Choi", "Geoffrey Oxberry", "Daniel White", "Trenton Kirchdoerfer" ], "comment": "20 pages, 5 figures, 5 tables, 5 algorithms", "categories": [ "math.NA", "cs.NA" ], "abstract": "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.", "revisions": [ { "version": "v1", "updated": "2019-09-25T07:46:32.000Z" } ], "analyses": { "keywords": [ "reduced order models", "accelerating design optimization", "design optimization acceleration approach", "model-based design optimization acceleration", "order model-based design optimization" ], "note": { "typesetting": "TeX", "pages": 20, "language": "en", "license": "arXiv", "status": "editable" } } }