Show simple item record

dc.contributor.authorSahoo, Sarojen_US
dc.contributor.authorLu, Yuzheen_US
dc.contributor.authorBerger, Matthewen_US
dc.contributor.editorBorgo, Ritaen_US
dc.contributor.editorMarai, G. Elisabetaen_US
dc.contributor.editorSchreck, Tobiasen_US
dc.date.accessioned2022-06-03T06:06:18Z
dc.date.available2022-06-03T06:06:18Z
dc.date.issued2022
dc.identifier.issn1467-8659
dc.identifier.urihttps://doi.org/10.1111/cgf.14549
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14549
dc.description.abstractIn this paper we present a reconstruction technique for the reduction of unsteady flow data based on neural representations of time-varying vector fields. Our approach is motivated by the large amount of data typically generated in numerical simulations, and in turn the types of data that domain scientists can generate in situ that are compact, yet useful, for post hoc analysis. One type of data commonly acquired during simulation are samples of the flow map, where a single sample is the result of integrating the underlying vector field for a specified time duration. In our work, we treat a collection of flow map samples for a single dataset as a meaningful, compact, and yet incomplete, representation of unsteady flow, and our central objective is to find a representation that enables us to best recover arbitrary flow map samples. To this end, we introduce a technique for learning implicit neural representations of time-varying vector fields that are specifically optimized to reproduce flow map samples sparsely covering the spatiotemporal domain of the data. We show that, despite aggressive data reduction, our optimization problem - learning a function-space neural network to reproduce flow map samples under a fixed integration scheme - leads to representations that demonstrate strong generalization, both in the field itself, and using the field to approximate the flow map. Through quantitative and qualitative analysis across different datasets we show that our approach is an improvement across a variety of data reduction methods, and across a variety of measures ranging from improved vector fields, flow maps, and features derived from the flow map.en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectCCS Concepts: Computing methodologies --> Neural networks; Reconstruction; Human-centered computing --> Scientific visualization
dc.subjectComputing methodologies
dc.subjectNeural networks
dc.subjectReconstruction
dc.subjectHuman centered computing
dc.subjectScientific visualization
dc.titleNeural Flow Map Reconstructionen_US
dc.description.seriesinformationComputer Graphics Forum
dc.description.sectionheadersAlgorithms and Machine Learning
dc.description.volume41
dc.description.number3
dc.identifier.doi10.1111/cgf.14549
dc.identifier.pages391-402
dc.identifier.pages12 pages


Files in this item

Thumbnail
Thumbnail

This item appears in the following Collection(s)

  • 41-Issue 3
    EuroVis 2022 - Conference Proceedings

Show simple item record