We propose a deep learning approach to collectively compare two or multiple ensembles,each of which is a collection of simulation outputs.The purpose of collective comparison is to help scientists understand differenc...We propose a deep learning approach to collectively compare two or multiple ensembles,each of which is a collection of simulation outputs.The purpose of collective comparison is to help scientists understand differences between simulation models by comparing their ensemble simulation outputs.However,the collective comparison is non-trivial because the spatiotemporal distributions of ensemble simulation outputs reside in a very high dimensional space.To this end,we choose to train a deep discriminative neural network to measure the dissimilarity between two given ensembles,and to identify when and where the two ensembles are different.We also design and develop a visualization system to help users understand the collective comparison results based on the discriminative network.We demonstrate the effectiveness of our approach with two real-world applications,including the ensemble comparison of the community atmosphere model(CAM)and the rapid radiative transfer model for general circulation models(RRTMG)for climate research,and the comparison of computational fluid dynamics(CFD)ensembles with different spatial resolutions.展开更多
基金US Department of Energy Los Alamos National Laboratory contract 47145 and UT-Battelle LLC contract 4000159447 program manager Laura Biven.
文摘We propose a deep learning approach to collectively compare two or multiple ensembles,each of which is a collection of simulation outputs.The purpose of collective comparison is to help scientists understand differences between simulation models by comparing their ensemble simulation outputs.However,the collective comparison is non-trivial because the spatiotemporal distributions of ensemble simulation outputs reside in a very high dimensional space.To this end,we choose to train a deep discriminative neural network to measure the dissimilarity between two given ensembles,and to identify when and where the two ensembles are different.We also design and develop a visualization system to help users understand the collective comparison results based on the discriminative network.We demonstrate the effectiveness of our approach with two real-world applications,including the ensemble comparison of the community atmosphere model(CAM)and the rapid radiative transfer model for general circulation models(RRTMG)for climate research,and the comparison of computational fluid dynamics(CFD)ensembles with different spatial resolutions.
