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Complex networks are used to model many real-world systems. However, thedimensionality of these systems can make them challenging to analyze.Dimensionality reduction techniques like POD can be used in such cases.However, these models are susceptible to perturbations in the input data. Wepropose an algorithmic framework that combines techniques from patternrecognition (PR) and stochastic filtering theory to enhance the output of suchmodels. The results of our study show that our method can improve the accuracyof the surrogate model under perturbed inputs. Deep Neural Networks (DNNs) aresusceptible to adversarial attacks. However, recent research has revealed thatNeural Ordinary Differential Equations (neural ODEs) exhibit robustness inspecific applications. We benchmark our algorithmic framework with the neuralODE-based approach as a reference.

Reinforcing POD-based model reduction techniques in reaction-diffusion complex networks using stochastic filtering and pattern recognition