LagNetViP: A Lagrangian Neural Network for Video Prediction

Understand

The dominant paradigms for video prediction rely on opaque transition models where neither the equations of motion nor the underlying physical quantities of the system are easily inferred.

• The equations of motion, as defined by Newton's second law, describe the time evolution of a physical system state and can therefore be applied toward the determination of future system states.
• In this paper, we introduce a video prediction model where the equations of motion are explicitly constructed from learned representations of the underlying physical quantities.
• To achieve this, we simultaneously learn a low-dimensional state representation and system Lagrangian.