Accurate forecasting of complex high-dimensional dynamical systems from observational data is essential for several applications across science and engineering. A key challenge, however, is that real-world measurements are often corrupted by noise, which severely degrades the performance of data-driven models. Particularly, in chaotic dynamical systems, where small errors amplify rapidly, it is challenging to identify a data-driven model from noisy data that achieves short-term accuracy while preserving long-term invariant properties. In this paper, we propose the use of the weak formulation as a complementary approach to the classical strong formulation of data-driven time-series forecasting models. Specifically, we focus on the neural ordinary differential equation (NODE) architecture. Unlike the standard strong formulation, which relies on the discretization of the NODE followed by optimization, the weak formulation constrains the model using a set of integrated residuals over temporal subdomains. While such a formulation yields an effective NODE model, we discover that the performance of a NODE can be further enhanced by employing this weak formulation as a penalty alongside the classical strong formulation-based learning. Through numerical demonstrations, we illustrate that our proposed training strategy, which we coined as the Weak-Penalty NODE (WP-NODE), achieves state-of-the-art forecasting accuracy and exceptional robustness across benchmark chaotic dynamical systems and real-world climate dataset.
View on arXiv