Uncertainty quantification for deeponets with ensemble kalman inversion

• URL: http://arxiv.org/abs/2403.03444v1
• Date: Wed, 6 Mar 2024 04:02:30 GMT
• Title: Uncertainty quantification for deeponets with ensemble kalman inversion
• Authors: Andrew Pensoneault, Xueyu Zhu
• Abstract summary: In this work, we propose a novel inference approach for efficient uncertainty quantification (UQ) for operator learning by harnessing the power of the Ensemble Kalman Inversion (EKI) approach. EKI is known for its derivative-free, noise-robust, and highly parallelizable feature, and has demonstrated its advantages for UQ for physics-informed neural networks. We deploy a mini-batch variant of EKI to accommodate larger datasets, mitigating the computational demand due to large datasets during the training stage.
• Score: 0.8158530638728501
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, operator learning, particularly the DeepONet, has received much attention for efficiently learning complex mappings between input and output functions across diverse fields. However, in practical scenarios with limited and noisy data, accessing the uncertainty in DeepONet predictions becomes essential, especially in mission-critical or safety-critical applications. Existing methods, either computationally intensive or yielding unsatisfactory uncertainty quantification, leave room for developing efficient and informative uncertainty quantification (UQ) techniques tailored for DeepONets. In this work, we proposed a novel inference approach for efficient UQ for operator learning by harnessing the power of the Ensemble Kalman Inversion (EKI) approach. EKI, known for its derivative-free, noise-robust, and highly parallelizable feature, has demonstrated its advantages for UQ for physics-informed neural networks [28]. Our innovative application of EKI enables us to efficiently train ensembles of DeepONets while obtaining informative uncertainty estimates for the output of interest. We deploy a mini-batch variant of EKI to accommodate larger datasets, mitigating the computational demand due to large datasets during the training stage. Furthermore, we introduce a heuristic method to estimate the artificial dynamics covariance, thereby improving our uncertainty estimates. Finally, we demonstrate the effectiveness and versatility of our proposed methodology across various benchmark problems, showcasing its potential to address the pressing challenges of uncertainty quantification in DeepONets, especially for practical applications with limited and noisy data.

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