SurroFlow: A Flow-Based Surrogate Model for Parameter Space Exploration and Uncertainty Quantification
<|reference_start|>SurroFlow: A Flow-Based Surrogate Model for Parameter Space Exploration and Uncertainty Quantification: 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.<|reference_end|>
@article{shen2024surroflow:,
    title={SurroFlow: A Flow-Based Surrogate Model for Parameter Space Exploration
  and Uncertainty Quantification},
    author={Jingyi Shen, Yuhan Duan, and Han-Wei Shen},
    journal={arXiv preprint arXiv:2407.12884},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.12884},
    primaryClass={cs.LG cs.AI cs.CV cs.GR cs.HC}
}