Browsing by Author "Xue, Tao"

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    A-ULMPM: An Adaptively Updated Lagrangian Material Point Method for Efficient Physics Simulation without Numerical Fracture
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Su, Haozhe; Xue, Tao; Han, Chengguizi; Aanjaneya, Mridul; Chaine, Raphaƫlle; Kim, Min H.
    We present an adaptively updated Lagrangian Material Point Method (A-ULMPM) to alleviate non-physical artifacts, such as the cell-crossing instability and numerical fracture, that plague state-of-the-art Eulerian formulations of MPM, while still allowing for large deformations that arise in fluid simulations. A-ULMPM spans MPM discretizations from total Lagrangian formulations to Eulerian formulations. We design an easy-to-implement physics-based criterion that allows A-ULMPM to update the reference configuration adaptively for measuring physical states, including stress, strain, interpolation kernels and their derivatives. For better efficiency and conservation of angular momentum, we further integrate the APIC [JSS*15] and MLS-MPM [HFG*18] formulations in A-ULMPM by augmenting the accuracy of velocity rasterization using both the local velocity and its first-order derivatives. Our theoretical derivations use a nodal discretized Lagrangian, instead of the weak form discretization in MLS-MPM [HFG*18], and naturally lead to a ''modified'' MLS-MPM in A-ULMPM, which can recover MLS-MPM using a completely Eulerian formulation. A-ULMPM does not require significant changes to traditional Eulerian formulations of MPM, and is computationally more efficient since it only updates interpolation kernels and their derivatives during large topology changes. We present end-to-end 3D simulations of stretching and twisting hyperelastic solids, viscous flows, splashing liquids, and multi-material interactions with large deformations to demonstrate the efficacy of our new method.
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    A Generalized Constitutive Model for Versatile MPM Simulation and Inverse Learning with Differentiable Physics
    (ACM Association for Computing Machinery, 2023) Su, Haozhe; Li, Xuan; Xue, Tao; Jiang, Chenfanfu; Aanjaneya, Mridul; Wang, Huamin; Ye, Yuting; Victor Zordan
    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.