Estimating Nonlinear Dynamics with the ConvNet Smoother

Understand

Estimating the state of a dynamical system from a series of noise-corrupted observations is fundamental in many areas of science and engineering.

• The most well-known method, the Kalman smoother (and the related Kalman filter), relies on assumptions of linearity and Gaussianity that are rarely met in practice.
• In this paper, we introduced a new dynamical smoothing method that exploits the remarkable capabilities of convolutional neural networks to approximate complex non-linear functions.
• The main idea is to generate a training set composed of both latent states and observations from an ensemble of simulators and to train the deep network to recover the former from the latter.