A Generalized Constitutive Model for Versatile MPM Simulation and Inverse Learning with Differentiable Physics

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dc.contributor.authorSu, Haozheen_US
dc.contributor.authorLi, Xuanen_US
dc.contributor.authorXue, Taoen_US
dc.contributor.authorJiang, Chenfanfuen_US
dc.contributor.authorAanjaneya, Mridulen_US
dc.contributor.editorWang, Huaminen_US
dc.contributor.editorYe, Yutingen_US
dc.contributor.editorVictor Zordanen_US
dc.date.accessioned2023-10-16T12:33:47Z
dc.date.available2023-10-16T12:33:47Z
dc.date.issued2023
dc.description.abstractWe present a generalized constitutive model for versatile physics simulation of inviscid fluids, Newtonian viscosity, hyperelasticity, viscoplasticity, elastoplasticity, and other physical effects that arise due to a mixture of these behaviors. The key ideas behind our formulation are the design of a generalized Kirchhoff stress tensor that can describe hyperelasticity, Newtonian viscosity and inviscid fluids, and the use of pre-projection and post-correction rules for simulating material behaviors that involve plasticity, including elastoplasticity and viscoplasticity.We show how our generalized Kirchhoff stress tensor can be coupled together into a generalized constitutive model that allows the simulation of diverse material behaviors by only changing parameter values. We present several side-by-side comparisons with physics simulations for specific constitutive models to show that our generalized model produces visually similar results. More notably, our formulation allows for inverse learning of unknown material properties directly from data using differentiable physics simulations. We present several 3D simulations to highlight the robustness of our method, even with multiple different materials. To the best of our knowledge, our approach is the first to recover the knowledge of unknown material properties without making explicit assumptions about the data.en_US
dc.description.number3
dc.description.sectionheadersFluids and Points
dc.description.seriesinformationProceedings of the ACM on Computer Graphics and Interactive Techniques
dc.description.volume6
dc.identifier.doi10.1145/3606925
dc.identifier.issn2577-6193
dc.identifier.urihttps://doi.org/10.1145/3606925
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1145/3606925
dc.publisherACM Association for Computing Machineryen_US
dc.subjectCCS Concepts: Computing methodologies -> Physical simulation generalized constitutive model, viscosity, elasticity, plasticity, material point method, differentiable physics"
dc.subjectComputing methodologies
dc.subjectPhysical simulation generalized constitutive model
dc.subjectviscosity
dc.subjectelasticity
dc.subjectplasticity
dc.subjectmaterial point method
dc.subjectdifferentiable physics"
dc.titleA Generalized Constitutive Model for Versatile MPM Simulation and Inverse Learning with Differentiable Physicsen_US
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SCA 2023: Eurographics/SIGGRAPH Symposium on Computer Animation