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Many real-world systems are modelled using complex ordinary differentialequations (ODEs). However, the dimensionality of these systems can make themchallenging to analyze. Dimensionality reduction techniques like ProperOrthogonal Decomposition (POD) can be used in such cases. However, thesereduced order models are susceptible to perturbations in the input. We proposea novel framework that combines machine learning and data assimilationtechniques to improving surrogate models to handle perturbations in input dataeffectively. Through rigorous experiments on dynamical systems modelled ongraphs, we demonstrate that our framework substantially improves the accuracyof surrogate models under input perturbations. Furthermore, we evaluate theframework's efficacy on alternative surrogate models, including neural ODEs,and the empirical results consistently show enhanced performance.

Improving Surrogate Model Robustness to Perturbations for Dynamical Systems Through Machine Learning and Data Assimilation