Variational Deep Learning for the Identification and Reconstruction of Chaotic and Stochastic Dynamical Systems from Noisy and Partial Observations

Understand

The data-driven recovery of the unknown governing equations of dynamical systems has recently received an increasing interest.

• However, the identification of governing equations remains challenging when dealing with noisy and partial observations.
• Here, we address this challenge and investigate variational deep learning schemes.
• Within the proposed framework, we jointly learn an inference model to reconstruct the true states of the system and the governing laws of these states from series of noisy and partial data.