Header

UZH-Logo

Maintenance Infos

A Surrogate Visualization Model Using the Tensor Train Format


Ballester-Ripoll, Rafael; Paredes, Enrique G; Pajarola, R (2016). A Surrogate Visualization Model Using the Tensor Train Format. In: Proceedings ACM SIGGRAPH ASIA Symposium on Visualization, Macao, 5 December 2016 - 8 December 2016, 13:1-8.

Abstract

Complex simulations and numerical experiments typically rely on a number of parameters and have an associated score function, e.g. with the goal of maximizing accuracy or minimizing computation time. However, the influence of each individual parameter is often poorly understood a priori and the joint parameter space can be difficult to explore, visualize and optimize. We model this space as an N-dimensional black-box tensor and apply a cross approximation strategy to sample it. Upon learning and compactly expressing this space as a surrogate visualization model, informative subspaces are interactively reconstructed and navigated in the form of charts, images, surface plots, etc. By exploiting efficient operations in the tensor train format, we are able to produce diagrams such as parallel coordinates, bivariate projections and dimensional stacking out of highly-compressed parameter spaces. We demonstrate the proposed framework with several scientific simulations that contain up to 6 parameters and billions of tensor grid points.

Abstract

Complex simulations and numerical experiments typically rely on a number of parameters and have an associated score function, e.g. with the goal of maximizing accuracy or minimizing computation time. However, the influence of each individual parameter is often poorly understood a priori and the joint parameter space can be difficult to explore, visualize and optimize. We model this space as an N-dimensional black-box tensor and apply a cross approximation strategy to sample it. Upon learning and compactly expressing this space as a surrogate visualization model, informative subspaces are interactively reconstructed and navigated in the form of charts, images, surface plots, etc. By exploiting efficient operations in the tensor train format, we are able to produce diagrams such as parallel coordinates, bivariate projections and dimensional stacking out of highly-compressed parameter spaces. We demonstrate the proposed framework with several scientific simulations that contain up to 6 parameters and billions of tensor grid points.

Statistics

Altmetrics

Downloads

16 downloads since deposited on 27 Dec 2016
16 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Language:English
Event End Date:8 December 2016
Deposited On:27 Dec 2016 08:14
Last Modified:30 Aug 2017 22:39
Publisher:ACM
ISBN:978-1-4503-4547-7
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1145/3002151.3002167
Other Identification Number:merlin-id:14368

Download

Preview Icon on Download
Content: Published Version
Filetype: PDF - Registered users only
Size: 2MB
View at publisher

Article Networks

TrendTerms

TrendTerms displays relevant terms of the abstract of this publication and related documents on a map. The terms and their relations were extracted from ZORA using word statistics. Their timelines are taken from ZORA as well. The bubble size of a term is proportional to the number of documents where the term occurs. Red, orange, yellow and green colors are used for terms that occur in the current document; red indicates high interlinkedness of a term with other terms, orange, yellow and green decreasing interlinkedness. Blue is used for terms that have a relation with the terms in this document, but occur in other documents.
You can navigate and zoom the map. Mouse-hovering a term displays its timeline, clicking it yields the associated documents.

Author Collaborations