Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network

TL;DR

This paper introduces a discrete-time conditional Gaussian Koopman network (CGKN) that learns surrogate models for high-dimensional nonlinear dynamical systems, enabling efficient state forecasting and data assimilation by exploiting latent linear dynamics.

Contribution

The work develops a novel CGKN framework that unifies scientific machine learning and data assimilation for partially observed nonlinear PDE systems, with analytical DA formulas and demonstrated effectiveness.

Findings

Achieves comparable performance to state-of-the-art SciML methods in state forecasting.

Provides efficient and accurate data assimilation for complex PDE systems.

Demonstrates applicability to turbulent and intermittent systems like Navier-Stokes.

Abstract

A discrete-time conditional Gaussian Koopman network (CGKN) is developed in this work to learn surrogate models that can perform efficient state forecast and data assimilation (DA) for high-dimensional complex dynamical systems, e.g., systems governed by nonlinear partial differential equations (PDEs). Focusing on nonlinear partially observed systems that are common in many engineering and earth science applications, this work exploits Koopman embedding to discover a proper latent representation of the unobserved system states, such that the dynamics of the latent states are conditional linear, i.e., linear with the given observed system states. The modeled system of the observed and latent states then becomes a conditional Gaussian system, for which the posterior distribution of the latent states is Gaussian and can be efficiently evaluated via analytical formulae. The analytical formulae of DA facilitate the incorporation of DA performance into the learning process of the modeled system, which leads to a framework that unifies scientific machine learning (SciML) and data assimilation. The performance of discrete-time CGKN is demonstrated on several canonical problems governed by nonlinear PDEs with intermittency and turbulent features, including the viscous Burgers' equation, the Kuramoto-Sivashinsky equation, and the 2-D Navier-Stokes equations, with which we show that the discrete-time CGKN framework achieves comparable performance as the state-of-the-art SciML methods in state forecast and provides efficient and accurate DA results. The discrete-time CGKN framework also serves as an example to illustrate unifying the development of SciML models and their other outer-loop applications such as design optimization, inverse problems, and optimal control.