Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning

Understand

Deep learning has achieved astonishing results on many tasks with large amounts of data and generalization within the proximity of training data.

• For many important real-world applications, these requirements are unfeasible and additional prior knowledge on the task domain is required to overcome the resulting problems.
• In particular, learning physics models for model-based control requires robust extrapolation from fewer samples - often collected online in real-time - and model errors may lead to drastic damages of the system.
• Directly incorporating physical insight has enabled us to obtain a novel deep model learning approach that extrapolates well while requiring fewer samples.