A deep learning based surrogate model for stochastic simulators

• URL: http://arxiv.org/abs/2110.13809v1
• Date: Sun, 24 Oct 2021 11:38:47 GMT
• Title: A deep learning based surrogate model for stochastic simulators
• Authors: Akshay Thakur and Souvik Chakraborty
• Abstract summary: We propose a deep learning-based surrogate model for simulators. We utilize conditional maximum mean discrepancy (CMMD) as the loss-function. Results obtained indicate the excellent performance of the proposed approach.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We propose a deep learning-based surrogate model for stochastic simulators. The basic idea is to use generative neural network to approximate the stochastic response. The challenge with such a framework resides in designing the network architecture and selecting loss-function suitable for stochastic response. While we utilize a simple feed-forward neural network, we propose to use conditional maximum mean discrepancy (CMMD) as the loss-function. CMMD exploits the property of reproducing kernel Hilbert space and allows capturing discrepancy between the between the target and the neural network predicted distributions. The proposed approach is mathematically rigorous, in the sense that it makes no assumptions about the probability density function of the response. Performance of the proposed approach is illustrated using four benchmark problems selected from the literature. Results obtained indicate the excellent performance of the proposed approach.

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