Fast and Robust Stochastic Structural Optimization

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Date
2020
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association and John Wiley & Sons Ltd.
Abstract
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., 26x34x28). 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.
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@article{
10.1111:cgf.13938
, journal = {Computer Graphics Forum}, title = {{
Fast and Robust Stochastic Structural Optimization
}}, author = {
Cui, Qiaodong
and
Langlois, Timothy
and
Sen, Pradeep
and
Kim, Theodore
}, year = {
2020
}, publisher = {
The Eurographics Association and John Wiley & Sons Ltd.
}, ISSN = {
1467-8659
}, DOI = {
10.1111/cgf.13938
} }
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