CECAV-DNN:Collective Ensemble Comparison and Visualization using Deep Neural Networks

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.

Hierarchical Visual Analysis and Steering Framework for Astrophysical Simulations

A framework for accelerating modern long-running astrophysical simulations is presented, which is based on a hierarchical architecture where computational steering in the high-resolution run is performed under the guide of knowledge obtained in the gradually refined ensemble analyses. Several visualization schemes for facilitating ensemble management, error analysis, parameter grouping and tuning are also integrated owing to the pluggable modular design. The proposed approach is prototyped based on the Flash code, and it can be extended by introducing userdefined visualization for specific requirements. Two real-world simulations, i.e., stellar wind and supernova remnant, are carried out to verify the proposed approach.

TBSSvis:Visual analytics for Temporal Blind Source Separation

Temporal Blind Source Separation(TBSS)is used to obtain the true underlying processes from noisy temporal multivariate data,such as electrocardiograms.TBSS has similarities to Principal Component Analysis(PCA)as it separates the input data into univariate components and is applicable to suitable datasets from various domains,such as medicine,finance,or civil engineering.Despite TBSS’s broad applicability,the involved tasks are not well supported in current tools,which offer only text-based interactions and single static images.Analysts are limited in analyzing and comparing obtained results,which consist of diverse data such as matrices and sets of time series.Additionally,parameter settings have a big impact on separation performance,but as a consequence of improper tooling,analysts currently do not consider the whole parameter space.We propose to solve these problems by applying visual analytics(VA)principles.Our primary contribution is a design study for TBSS,which so far has not been explored by the visualization community.We developed a task abstraction and visualization design in a user-centered design process.Task-specific assembling of well-established visualization techniques and algorithms to gain insights in the TBSS processes is our secondary contribution.We present TBSSvis,an interactive web-based VA prototype,which we evaluated extensively in two interviews with five TBSS experts.Feedback and observations from these interviews show that TBSSvis supports the actual workflow and combination of interactive visualizations that facilitate the tasks involved in analyzing TBSS results.