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Title: PlasticityNet: Learning to Simulate Metal, Sand, and Snow for Optimization Time Integration
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.  more » « less
Award ID(s):
2006570
PAR ID:
10466420
Author(s) / Creator(s):
; ; ; ; ;
Date Published:
Journal Name:
Advances in Neural Information Processing Systems
Volume:
35
Page Range / eLocation ID:
27783-27796
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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