Interpolated Adaptive Linear Reduced Order Modeling for Deformation Dynamics

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dc.contributor.authorTao, Yutian
dc.contributor.authorChiaramonte, Maurizio
dc.contributor.authorFernandez, Pablo
dc.contributor.editorMasia, Belen
dc.contributor.editorThies, Justus
dc.date.accessioned2026-04-17T12:27:10Z
dc.date.available2026-04-17T12:27:10Z
dc.date.issued2026
dc.description.abstractLinear reduced-order modeling (ROM) is widely used for efficient simulation of deformation dynamics, but its accuracy is often limited by the fixed linearization of the reduced mapping. We propose a new adaptive strategy for linear ROM that allows the reduced mapping to vary dynamically in response to the evolving deformation state, significantly improving accuracy over traditional linear approaches. To further handle large deformations, we introduce a historical displacement basis combined with Grassmann interpolation, enabling the system to recover robustly even in challenging scenarios. We evaluate our method through quantitative online-error analysis and qualitative comparisons with principal component analysis (PCA)-based linear ROM simulations, demonstrating substantial accuracy gains while preserving comparable computational costs.
dc.description.number2
dc.description.sectionheadersSolving Deformation: Numerical Methods for Elastic Simulation
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume45
dc.identifier.doi10.1111/cgf.70371
dc.identifier.issn1467-8659
dc.identifier.pages10 pages
dc.identifier.urihttps://diglib.eg.org/handle/10.1111/cgf70371
dc.identifier.urihttps://doi.org/10.1111/cgf.70371
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.
dc.rightsCC-BY-4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectPhysical simulation
dc.subjectDimensionality reduction
dc.subjectManifold learning
dc.titleInterpolated Adaptive Linear Reduced Order Modeling for Deformation Dynamics
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