In this paper, we cover how to learn a Lagrangian system through a neural net. This is especially interesting as neural nets typically don’t learn symmetries very well, even though they are prevalent phenomenon in our natural world.

• What are some symmetries and conservation laws the LNN was successful in learning in your experiments?
• What makes a Lagrangian system a black box?
• If CNNs can learn to become rotationally invariant using augmentation, can we leverage those methods to learn symmetries in a LNN?