Efficient Generation of Multimodal Fluid Simulation Data

Simple item page
dc.contributor.authorBaieri, Danieleen_US
dc.contributor.authorCrisostomi, Donatoen_US
dc.contributor.authorEsposito, Stefanoen_US
dc.contributor.authorMaggioli, Filippoen_US
dc.contributor.authorRodolà, Emanueleen_US
dc.contributor.editorCaputo, Arielen_US
dc.contributor.editorGarro, Valeriaen_US
dc.contributor.editorGiachetti, Andreaen_US
dc.contributor.editorCastellani, Umbertoen_US
dc.contributor.editorDulecha, Tinsae Gebrechristosen_US
dc.date.accessioned2024-11-11T12:47:50Z
dc.date.available2024-11-11T12:47:50Z
dc.date.issued2024
dc.description.abstractIn this work, we introduce an efficient and intuitive framework to produce synthetic multi-modal datasets of fluid simulations. The proposed pipeline can reproduce the dynamics of fluid flows and allows for exploring and learning various properties of their complex behavior from distinct perspectives and modalities. We aim to exploit these properties to fulfill the community's need for standardized training data, fostering more reproducible and robust research. We employ our framework to generate a set of thoughtfully designed training datasets, which attempt to span specific fluid simulation scenarios meaningfully. We demonstrate the properties of our contributions by evaluating recently published algorithms for the neural fluid simulation and fluid inverse rendering tasks using our benchmark datasets.en_US
dc.description.sectionheadersVirtual Training and Simulation
dc.description.seriesinformationSmart Tools and Applications in Graphics - Eurographics Italian Chapter Conference
dc.identifier.doi10.2312/stag.20241332
dc.identifier.isbn978-3-03868-265-3
dc.identifier.issn2617-4855
dc.identifier.pages10 pages
dc.identifier.urihttps://doi.org/10.2312/stag.20241332
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/stag20241332
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies → Physical simulation; Computer graphics
dc.subjectComputing methodologies → Physical simulation
dc.subjectComputer graphics
dc.titleEfficient Generation of Multimodal Fluid Simulation Dataen_US
Files
Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
stag20241332.pdf
Size:
22.12 MB
Format:
Adobe Portable Document Format
Download
Collections
Italian Chapter Conference 2024 - Smart Tools and Apps in Graphics