A Generalized Constitutive Model for Versatile MPM Simulation and Inverse Learning with Differentiable Physics
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Date
2023
Journal Title
Journal ISSN
Volume Title
Publisher
ACM Association for Computing Machinery
Abstract
We 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.
Description
CCS Concepts: Computing methodologies -> Physical simulation generalized constitutive model, viscosity, elasticity, plasticity, material point method, differentiable physics"
@inproceedings{10.1145:3606925,
booktitle = {Proceedings of the ACM on Computer Graphics and Interactive Techniques},
editor = {Wang, Huamin and Ye, Yuting and Victor Zordan},
title = {{A Generalized Constitutive Model for Versatile MPM Simulation and Inverse Learning with Differentiable Physics}},
author = {Su, Haozhe and Li, Xuan and Xue, Tao and Jiang, Chenfanfu and Aanjaneya, Mridul},
year = {2023},
publisher = {ACM Association for Computing Machinery},
ISSN = {2577-6193},
ISBN = {},
DOI = {10.1145/3606925}
}