A deep learning based surrogate model for stochastic simulators

نویسندگان

چکیده

We propose a deep learning-based surrogate model for stochastic simulators. The basic idea is to use generative neural network approximate the response. challenge with such framework resides in designing architecture and selecting loss-function suitable While we utilize simple feed-forward network, conditional maximum mean discrepancy (CMMD) as loss function. CMMD exploits property of reproducing kernel Hilbert space allows capturing between target predicted distributions. proposed approach mathematically rigorous, sense that it makes no assumptions about probability density function performance illustrated using four benchmark problems selected from literature. Results obtained indicate excellent approach.

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ژورنال

عنوان ژورنال: Probabilistic Engineering Mechanics

سال: 2022

ISSN: ['1878-4275', '0266-8920']

DOI: https://doi.org/10.1016/j.probengmech.2022.103248