title: Constraint-based graph network simulator
abstract: In the rapidly advancing area of learned physical simulators, nearly all methods train a forward model that directly predicts future states from input states. However, many traditional simulation engines use a constraint-based approach instead of direct prediction. Here we present a framework for constraint-based learned simulation, where a scalar constraint function is implemented as a trainable function approximator, and future predictions are computed as the solutions to a constraint satisfaction problem. We implement our method using a graph neural network as the constraint function and gradient descent as the constraint solver. The architecture can be trained by standard backpropagation. We test the model on a variety of challenging physical domains, including simulated ropes, bouncing balls, colliding irregular shapes and splashing fluids. Our model achieves better or comparable performance to top learned simulators. A key advantage of our model is the ability to generalize to more solver iterations at test time to improve the simulation accuracy. We also show how hand-designed constraints can be added at test time to satisfy objectives which were not present in the training data, which is not possible with forward approaches. Our constraint-based framework is applicable to any setting in which forward learned simulators are used, and more generally demonstrates key ways that learned models can leverage popular methods in numerical methods.
authors: Yulia Rubanova Alvaro Sanchez-Gonzalez Tobias Pfaff Peter Battaglia
hIndex: Peter Battaglia 40