TY - JOUR
T1 - DLA-VPS
T2 - Deep-Learning-Assisted Visual Parameter Space Analysis of Cosmological Simulations
AU - Sun, Cheng
AU - Wang, Ko Chih
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85129578310&partnerID=8YFLogxK
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U2 - 10.1109/MCG.2022.3169554
DO - 10.1109/MCG.2022.3169554
M3 - Article
C2 - 35471878
AN - SCOPUS:85129578310
SN - 0272-1716
VL - 42
SP - 41
EP - 52
JO - IEEE Computer Graphics and Applications
JF - IEEE Computer Graphics and Applications
IS - 3
ER -