Efficient Localization of Discontinuities in Complex Computational Simulations
Author(s)
Gorodetsky, Alex Arkady; Marzouk, Youssef M.
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Surrogate models for computational simulations are input-output approximations that allow computationally intensive analyses, such as uncertainty propagation and inference, to be performed efficiently. When a simulation output does not depend smoothly on its inputs, the error and convergence rate of many approximation methods deteriorate substantially. This paper details a method for efficiently localizing discontinuities in the input parameter domain, so that the model output can be approximated as a piecewise smooth function. The approach comprises an initialization phase, which uses polynomial annihilation to assign function values to different regions and thus seed an automated labeling procedure, followed by a refinement phase that adaptively updates a kernel support vector machine representation of the separating surface via active learning. The overall approach avoids structured grids and exploits any available simplicity in the geometry of the separating surface, thus reducing the number of model evaluations required to localize the discontinuity. The method is illustrated on examples of up to eleven dimensions, including algebraic models and ODE/PDE systems, and demonstrates improved scaling and efficiency over other discontinuity localization approaches.
Date issued
2014-11Department
Massachusetts Institute of Technology. Department of Aeronautics and AstronauticsJournal
SIAM Journal on Scientific Computing
Publisher
Society for Industrial and Applied Mathematics
Citation
Gorodetsky, Alex, and Youssef Marzouk. “Efficient Localization of Discontinuities in Complex Computational Simulations.” SIAM Journal on Scientific Computing 36, no. 6 (January 2014): A2584–A2610. © 2014, Society for Industrial and Applied Mathematics
Version: Final published version
ISSN
1064-8275
1095-7197