CGKN: A Deep Learning Framework for Modeling Complex Dynamical Systems and Efficient Data Assimilation

TL;DR

The paper introduces CGKN, a deep learning framework that models complex nonlinear dynamical systems and integrates data assimilation efficiently, addressing challenges of traditional ensemble methods and enabling accurate forecasting with uncertainty quantification.

Contribution

CGKN transforms nonlinear systems into neural differential equations with Gaussian structures, facilitating seamless data assimilation integration and capturing extreme events in complex systems.

Findings

Effective prediction on turbulent systems

Robust data assimilation without empirical tuning

Captures non-Gaussian features and extreme events

Abstract

Deep learning is widely used to predict complex dynamical systems in many scientific and engineering areas. However, the black-box nature of these deep learning models presents significant challenges for carrying out simultaneous data assimilation (DA), which is a crucial technique for state estimation, model identification, and reconstructing missing data. Integrating ensemble-based DA methods with nonlinear deep learning models is computationally expensive and may suffer from large sampling errors. To address these challenges, we introduce a deep learning framework designed to simultaneously provide accurate forecasts and efficient DA. It is named Conditional Gaussian Koopman Network (CGKN), which transforms general nonlinear systems into nonlinear neural differential equations with conditional Gaussian structures. CGKN aims to retain essential nonlinear components while applying systematic and minimal simplifications to facilitate the development of analytic formulae for nonlinear DA. This allows for seamless integration of DA performance into the deep learning training process, eliminating the need for empirical tuning as required in ensemble methods. CGKN compensates for structural simplifications by lifting the dimension of the system, which is motivated by Koopman theory. Nevertheless, CGKN exploits special nonlinear dynamics within the lifted space. This enables the model to capture extreme events and strong non-Gaussian features in joint and marginal distributions with appropriate uncertainty quantification. We demonstrate the effectiveness of CGKN for both prediction and DA on three strongly nonlinear and non-Gaussian turbulent systems: the projected stochastic Burgers-Sivashinsky equation, the Lorenz 96 system, and the El Ni\~no-Southern Oscillation. The results justify the robustness and computational efficiency of CGKN.