2020

Lagrangian Neural Networks

Cranmer, Miles, Greydanus, Sam, Hoyer, Stephan et al.

Understand

Accurate models of the world are built upon notions of its underlying symmetries.

In physics, these symmetries correspond to conservation laws, such as for energy and momentum.
Yet even though neural network models see increasing use in the physical sciences, they struggle to learn these symmetries.
In this paper, we propose Lagrangian Neural Networks (LNNs), which can parameterize arbitrary Lagrangians using neural networks.

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