Estimating Failure Probability with Neural Operator Hybrid Approach

Mujing Li, Yani Feng and Guanjie Wang ()
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Mujing Li: School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China
Yani Feng: School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China
Guanjie Wang: School of Statistics and Mathematics, Shanghai Lixin University of Accounting and Finance, Shanghai 201209, China

Mathematics, 2023, vol. 11, issue 12, 1-15

Abstract: Evaluating failure probability for complex engineering systems is a computationally intensive task. While the Monte Carlo method is easy to implement, it converges slowly and, hence, requires numerous repeated simulations of a complex system to generate sufficient samples. To improve the efficiency, methods based on surrogate models are proposed to approximate the limit state function. In this work, we reframe the approximation of the limit state function as an operator learning problem and utilize the DeepONet framework with a hybrid approach to estimate the failure probability. The numerical results show that our proposed method outperforms the prior neural hybrid method.

Keywords: failure probability; neural operator learning; DeepONet; approximation theory (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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