SurroFlow: A Flow-Based Surrogate Model for Parameter Space Exploration and Uncertainty Quantification
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866917725453942784 |
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| author | Shen, Jingyi Duan, Yuhan Shen, Han-Wei |
| author_facet | Shen, Jingyi Duan, Yuhan Shen, Han-Wei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_12884 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SurroFlow: A Flow-Based Surrogate Model for Parameter Space Exploration and Uncertainty Quantification Shen, Jingyi Duan, Yuhan Shen, Han-Wei Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition Graphics Human-Computer Interaction 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. |
| title | SurroFlow: A Flow-Based Surrogate Model for Parameter Space Exploration and Uncertainty Quantification |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition Graphics Human-Computer Interaction |
| url | https://arxiv.org/abs/2407.12884 |