A Generalized Constitutive Model for Versatile MPM Simulation and Inverse Learning with Differentiable Physics
A Generalized Constitutive Model for Versatile MPM Simulation and Inverse Learning with Differentiable Physics
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DOI：
10.1145/3606925
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发表时间：
2023-08
影响因子：
1.3
通讯作者：
Haozhe Su;Xuan Li;Tao Xue;Chenfanfu Jiang;Mridul Aanjaneya
中科院分区：
文献类型：
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作者：
Haozhe Su;Xuan Li;Tao Xue;Chenfanfu Jiang;Mridul Aanjaneya
我们提出了一个通用的物理模拟的无粘流体，牛顿粘度，超弹性，粘塑性，弹塑性，以及其他物理效应，由于这些行为的混合物的广义本构模型。我们的公式背后的关键思想是广义基尔霍夫应力张量的设计，可以描述超弹性，牛顿粘度和无粘流体，并使用预投影和后校正规则模拟材料的行为，涉及塑性，包括弹塑性和粘塑性。我们展示了我们的广义基尔霍夫应力张量可以耦合在一起，成为一个广义的本构模型，允许模拟不同的材料行为，只改变参数值。我们提出了几个并排的比较与物理模拟特定的本构模型，以表明我们的广义模型产生视觉上相似的结果。更值得注意的是，我们的公式允许使用可微物理模拟直接从数据中反向学习未知的材料特性。我们提出了几个3D模拟，以突出我们的方法的鲁棒性，即使有多种不同的材料。据我们所知，我们的方法是第一个在不对数据进行明确假设的情况下恢复未知材料属性的知识。
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.