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dc.contributor.authorChen, Yu Ju (Edwin)en_US
dc.contributor.authorLevin, David I. W.en_US
dc.contributor.authorKaufmann, Dannyen_US
dc.contributor.authorAscher, Urien_US
dc.contributor.authorPai, Dinesh K.en_US
dc.contributor.editorBatty, Christopher and Huang, Jinen_US
dc.date.accessioned2019-11-22T13:23:07Z
dc.date.available2019-11-22T13:23:07Z
dc.date.issued2019
dc.identifier.isbn978-1-4503-6677-9
dc.identifier.issn1727-5288
dc.identifier.urihttps://doi.org/10.1145/3309486.3340248
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1145/3309486-3340248
dc.description.abstractElastodynamic system simulation is a key procedure in computer graphics and robotics applications. To enable these simulations, the governing differential system is discretized in space (employing FEM) and then in time. For many simulation-based applications keeping the spatial resolution of the computational mesh effectively coarse is crucial for securing acceptable computational efficiency. However, this can introduce numerical stiffening effects that impede visual accuracy. We propose and demonstrate, for both linear and nonlinear force models, a new method called EigenFit that improves the consistency and accuracy of the lower energy, primary deformation modes, as the spatial mesh resolution is coarsened. EigenFit applies a partial spectral decomposition, solving a generalized eigenvalue problem in the leading mode subspace and then replacing the first several eigenvalues of the coarse mesh by those of the fine one at rest. EigenFit's performance relies on a novel subspace model reduction technique which restricts the spectral decomposition to finding just a few of the leading eigenmodes. We demonstrate its efficacy on a number of objects with both homogenous and heterogenous material distributions.en_US
dc.publisherACMen_US
dc.subjectComputing methodologies→Physical simulation. Elastodynamic simulation
dc.subjectnumerical stiffening
dc.titleEigenFit for Consistent Elastodynamic Simulation Across Mesh Resolutionen_US
dc.description.seriesinformationEurographics/ ACM SIGGRAPH Symposium on Computer Animation
dc.description.sectionheadersLearning and Simulation
dc.identifier.doi10.1145/3309486.3340248


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