Incorporating Symmetry into Deep Dynamics Models for Improved Generalization

Understand

Recent work has shown deep learning can accelerate the prediction of physical dynamics relative to numerical solvers.

• However, limited physical accuracy and an inability to generalize under distributional shift limit its applicability to the real world.
• We propose to improve accuracy and generalization by incorporating symmetries into convolutional neural networks.
• Specifically, we employ a variety of methods each tailored to enforce a different symmetry.