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Fast and Robust Stochastic Structural Optimization
Author(s) -
Cui Qiaodong,
Langlois Timothy,
Sen Pradeep,
Kim Theodore
Publication year - 2020
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.13938
Subject(s) - maxima and minima , computation , computer science , quadratic equation , mathematical optimization , stochastic optimization , algorithm , inertia , mathematics , mathematical analysis , physics , geometry , classical mechanics
Stochastic structural analysis can assess whether a fabricated object will break under real‐world conditions. While this approach is powerful, it is also quite slow, which has previously limited its use to coarse resolutions (e.g., 26 × 34 × 28 ). We show that this approach can be made asymptotically faster, which in practice reduces computation time by two orders of magnitude, and allows the use of previously‐infeasible resolutions. We achieve this by showing that the probability gradient can be computed in linear time instead of quadratic, and by using a robust new scheme that stabilizes the inertia gradients used by the optimization. Additionally, we propose a constrained restart method that deals with local minima, and a sheathing approach that further reduces the weight of the shape. Together, these components enable the discovery of previously‐inaccessible designs.