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

Cheng Sun, Ko Chih Wang

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)41-52
Number of pages12
JournalIEEE Computer Graphics and Applications
Volume42
Issue number3
DOIs
Publication statusPublished - 2022

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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