PlasticityNet: Learning to Simulate Metal, Sand, and Snow for Optimization Time Integration
PlasticityNet: Learning to Simulate Metal, Sand, and Snow for Optimization Time Integration
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发表时间：
2022
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通讯作者：
Xuan Li;Yadi Cao;Minchen Li;Yin Yang;Craig A. Schroeder;Chenfanfu Jiang
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作者：
Xuan Li;Yadi Cao;Minchen Li;Yin Yang;Craig A. Schroeder;Chenfanfu Jiang
在本文中，我们提出了一种基于神经网络的方法，用于学习表示从橡胶、金属到沙子和雪等塑性固体材料的行为。与弹簧力等弹力不同，这些塑性力并非源自任何势能的位置梯度，这对其模拟的稳定性和灵活性带来了巨大挑战。我们的方法通过学习一种可推广的塑性能量有效地解决了这一问题，该塑性能量的导数与塑性力的解析行为紧密匹配。我们的方法首次能够使用与时间步长无关、无条件稳定的基于优化的时间积分器来模拟各种任意的弹塑性组合。我们通过学习并生成具有复杂动力学的金属、沙子和雪的具有挑战性的二维和三维效果，证明了我们方法的有效性。
In this paper, we propose a neural network-based approach for learning to represent the behavior of plastic solid materials ranging from rubber and metal to sand and snow. Unlike elastic forces such as spring forces, these plastic forces do not result from the positional gradient of any potential energy, imposing great challenges on the stability and flexibility of their simulation. Our method effectively resolves this issue by learning a generalizable plastic energy whose derivative closely matches the analytical behavior of plastic forces. Our method, for the first time, enables the simulation of a wide range of arbitrary elasticity-plasticity combinations using time step-independent, unconditionally stable optimization-based time integrators. We demonstrate the efficacy of our method by learning and producing challenging 2D and 3D effects of metal, sand, and snow with complex dynamics.