Randomized prior wavelet neural operator for uncertainty quantification

• URL: http://arxiv.org/abs/2302.01051v1
• Date: Thu, 2 Feb 2023 12:28:49 GMT
• Title: Randomized prior wavelet neural operator for uncertainty quantification
• Authors: Shailesh Garg and Souvik Chakraborty
• Abstract summary: We propose a novel data-driven operator learning framework referred to as the textitRandomized Prior Wavelet Neural Operator (RP-WNO) The proposed RP-WNO is an extension of the recently proposed wavelet neural operator, which boasts excellent generalizing capabilities but cannot estimate the uncertainty associated with its predictions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this paper, we propose a novel data-driven operator learning framework referred to as the \textit{Randomized Prior Wavelet Neural Operator} (RP-WNO). The proposed RP-WNO is an extension of the recently proposed wavelet neural operator, which boasts excellent generalizing capabilities but cannot estimate the uncertainty associated with its predictions. RP-WNO, unlike the vanilla WNO, comes with inherent uncertainty quantification module and hence, is expected to be extremely useful for scientists and engineers alike. RP-WNO utilizes randomized prior networks, which can account for prior information and is easier to implement for large, complex deep-learning architectures than its Bayesian counterpart. Four examples have been solved to test the proposed framework, and the results produced advocate favorably for the efficacy of the proposed framework.

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