DLA-VPS: Deep-Learning-Assisted Visual Parameter Space Analysis of Cosmological Simulations

Cheng Sun, Ko Chih Wang

研究成果: 雜誌貢獻期刊論文同行評審

摘要

Cosmologists often build a mathematics simulation model to study the observed universe. However, running a high-fidelity simulation is time consuming and thus can inconvenience the analysis. This is especially so when the analysis involves trying out a large number of simulation input parameter configurations. Therefore, selecting an input parameter configuration that can meet the needs of an analysis task has become an important part of the analysis process. In this work, we propose an interactive visual system that efficiently helps users understand the parameter space related to their cosmological data. Our system utilizes a GAN-based surrogate model to reconstruct the simulation outputs without running the expensive simulation. We also extract information learned by the deep neural-network-based surrogate models to facilitate the parameter space exploration. We demonstrate the effectiveness of our system via multiple case studies. These case study results demonstrate valuable simulation input parameter configuration and subregion analyses.

原文英語
頁(從 - 到)41-52
頁數12
期刊IEEE Computer Graphics and Applications
42
發行號3
DOIs
出版狀態已發佈 - 2022

ASJC Scopus subject areas

  • 軟體
  • 電腦繪圖與電腦輔助設計

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