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Title: EXTREME LEARNING MACHINES FOR VARIANCE-BASED GLOBAL SENSITIVITY ANALYSIS
Variance-based global sensitivity analysis (GSA) can provide a wealth of information when applied to complex models.A well-known Achilles' heel of this approach is its computational cost, which often renders it unfeasible in practice. An appealing alternative is to instead analyze the sensitivity of a surrogate model with the goal of lowering computational costs while maintaining sufficient accuracy. Should a surrogate be simple enough to be amenable to the analytical calculations of its Sobol' indices, the cost of GSA is essentially reduced to the construction of the surrogate.We propose a new class of sparse-weight extreme learning machines (ELMs), which, when considered as surrogates in the context of GSA, admit analytical formulas for their Sobol' indices and, unlike the standard ELMs, yield accurate approximations of these indices. The effectiveness of this approach is illustrated through both traditional benchmarks in the field and on a chemical reaction network.  more » « less
Award ID(s):
1745654 1953271
PAR ID:
10520935
Author(s) / Creator(s):
; ;
Publisher / Repository:
Begel House Inc.
Date Published:
Journal Name:
International Journal for Uncertainty Quantification
Volume:
14
Issue:
4
ISSN:
2152-5080
Page Range / eLocation ID:
83 to 103
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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