Information-geometry of physics-informed statistical manifolds and its use in data assimilation

TL;DR

This paper introduces an information-geometric approach to data assimilation using statistical manifolds, leveraging discrepancy metrics like KL divergence and Wasserstein distance to improve efficiency and accuracy in forecasting dynamical systems.

Contribution

It develops a novel data assimilation method that exploits the geometry of statistical manifolds with discrepancy metrics, enhancing computational efficiency and reducing uncertainty.

Findings

Information geometry reduces computational cost in data assimilation.

KL divergence and Wasserstein metrics perform similarly when convergence is reached.

Deep neural networks accelerate the optimization process.

Abstract

The data-aware method of distributions (DA-MD) is a low-dimension data assimilation procedure to forecast the behavior of dynamical systems described by differential equations. It combines sequential Bayesian update with the MD, such that the former utilizes available observations while the latter propagates the (joint) probability distribution of the uncertain system state(s). The core of DA-MD is the minimization of a distance between an observation and a prediction in distributional terms, with prior and posterior distributions constrained on a statistical manifold defined by the MD. We leverage the information-geometric properties of the statistical manifold to reduce predictive uncertainty via data assimilation. Specifically, we exploit the information geometric structures induced by two discrepancy metrics, the Kullback-Leibler divergence and the Wasserstein distance, which explicitly yield natural gradient descent. To further accelerate optimization, we build a deep neural network as a surrogate model for the MD that enables automatic differentiation. The manifold's geometry is quantified without sampling, yielding an accurate approximation of the gradient descent direction. Our numerical experiments demonstrate that accounting for the information-geometry of the manifold significantly reduces the computational cost of data assimilation by facilitating the calculation of gradients and by reducing the number of required iterations. Both storage needs and computational cost depend on the dimensionality of a statistical manifold, which is typically small by MD construction. When convergence is achieved, the Kullback-Leibler and $L_2$ Wasserstein metrics have similar performances, with the former being more sensitive to poor choices of the prior.