InSituNet: Deep Image Synthesis for Parameter Space Exploration of Ensemble Simulations

Wenbin He, Junpeng Wang, Hanqi Guo, Ko Chih Wang, Han Wei Shen, Mukund Raj, Youssef S.G. Nashed, Tom Peterka

研究成果: 雜誌貢獻期刊論文同行評審

19 引文 斯高帕斯(Scopus)

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.

原文英語
文章編號8805426
頁(從 - 到)23-33
頁數11
期刊IEEE Transactions on Visualization and Computer Graphics
26
發行號1
DOIs
出版狀態已發佈 - 2020 一月
對外發佈

ASJC Scopus subject areas

  • 軟體
  • 訊號處理
  • 電腦視覺和模式識別
  • 電腦繪圖與電腦輔助設計

指紋

深入研究「InSituNet: Deep Image Synthesis for Parameter Space Exploration of Ensemble Simulations」主題。共同形成了獨特的指紋。

引用此