Web: http://arxiv.org/abs/2112.09161

Jan. 31, 2022, 2:11 a.m. | Yulia Rubanova, Alvaro Sanchez-Gonzalez, Tobias Pfaff, Peter Battaglia

cs.LG updates on arXiv.org arxiv.org

In the area of physical simulations, nearly all neural-network-based methods
directly predict future states from the input states. However, many traditional
simulation engines instead model the constraints of the system and select the
state which satisfies them. Here we present a framework for constraint-based
learned simulation, where a scalar constraint function is implemented as a
graph neural network, and future predictions are computed by solving the
optimization problem defined by the learned constraint. Our model achieves
comparable or better accuracy …

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