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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.

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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
Scopus Subject Areas:Physical Sciences > Computer Vision and Pattern Recognition
Physical Sciences > Human-Computer Interaction
Physical Sciences > Software
Physical Sciences > Computer Graphics and Computer-Aided Design
Language:English
Event End Date:8 December 2016
Deposited On:27 Dec 2016 08:14
Last Modified:13 May 2020 21:09
Publisher:ACM
ISBN:978-1-4503-4547-7
OA Status:Closed
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

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