SurroFlow: A Flow-Based Surrogate Model for Parameter Space Exploration and Uncertainty Quantification

• URL: http://arxiv.org/abs/2407.12884v1
• Date: Tue, 16 Jul 2024 19:08:49 GMT
• Title: SurroFlow: A Flow-Based Surrogate Model for Parameter Space Exploration and Uncertainty Quantification
• Authors: Jingyi Shen, Yuhan Duan, Han-Wei Shen,
• Abstract summary: Existing deep learning-based surrogate models facilitate efficient data generation, but fall short in uncertainty quantification, efficient parameter space exploration, and reverse prediction. We introduce SurroFlow, a novel normalizing flow-based surrogate model, to learn the invertible transformation between simulation parameters and simulation outputs. Our framework significantly reduces the computational costs while enhancing the reliability and exploration capabilities of scientific surrogate models.
• Score: 17.175947741031674
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing deep learning-based surrogate models facilitate efficient data generation, but fall short in uncertainty quantification, efficient parameter space exploration, and reverse prediction. In our work, we introduce SurroFlow, a novel normalizing flow-based surrogate model, to learn the invertible transformation between simulation parameters and simulation outputs. The model not only allows accurate predictions of simulation outcomes for a given simulation parameter but also supports uncertainty quantification in the data generation process. Additionally, it enables efficient simulation parameter recommendation and exploration. We integrate SurroFlow and a genetic algorithm as the backend of a visual interface to support effective user-guided ensemble simulation exploration and visualization. Our framework significantly reduces the computational costs while enhancing the reliability and exploration capabilities of scientific surrogate models.

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