InSituNet：用于环绕仿真参数空间探索的深度图像合成
InSituNet: Deep Image Synthesis for Parameter Space Exploration of Ensemble Simulations
摘要：
我们提出了InSituNet，这是一种基于深度学习的代理模型，用于支持原位可视化集成模拟的参数空间探索。由于I/O和存储限制，在模拟时生成可视化的原位可视化在处理大规模模拟中变得越来越普遍。然而，原位可视化方法限制了事后勘探的灵活性，因为原始模拟数据不再可用。虽然已经提出了多种基于图像的方法来减轻这种限制，但这些方法缺乏探索模拟参数的能力。我们的方法允许通过利用深度学习的最新进展来灵活探索大规模集成模拟的参数空间。具体来说，我们将InSituNet设计为卷积回归模型，以学习从模拟和可视化参数到可视化结果的映射。使用经过训练的模型，用户可以在各种可视化设置下为不同的模拟参数生成新的图像，从而可以深入分析底层的集合模拟。我们证明了InSituNet在燃烧，宇宙学和海洋模拟通过定量和定性评估的有效性。
英文摘要：
We propose InSituNet, a deep learning based surrogate model to support parameter space exploration for ensemble simulations that are visualized in situ. In situ visualization, generating visualizations at simulation time, is becoming prevalent in handling large-scale simulations because of the I/O and storage constraints. However, in situ visualization approaches limit the flexibility of post-hoc exploration because the raw simulation data are no longer available. Although multiple image-based approaches have been proposed to mitigate this limitation, those approaches lack the ability to explore the simulation parameters. Our approach allows flexible exploration of parameter space for large-scale ensemble simulations by taking advantage of the recent advances in deep learning. Specifically, we design InSituNet as a convolutional regression model to learn the mapping from the simulation and visualization parameters to the visualization results. With the trained model, users can generate new images for different simulation parameters under various visualization settings, which enables in-depth analysis of the underlying ensemble simulations. We demonstrate the effectiveness of InSituNet in combustion, cosmology, and ocean simulations through quantitative and qualitative evaluations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
