Reinforcing POD-based model reduction techniques in reaction-diffusion
complex networks using stochastic filtering and pattern recognition
- URL: http://arxiv.org/abs/2307.09762v2
- Date: Sat, 16 Sep 2023 14:09:43 GMT
- Title: Reinforcing POD-based model reduction techniques in reaction-diffusion
complex networks using stochastic filtering and pattern recognition
- Authors: Abhishek Ajayakumar, Soumyendu Raha
- Abstract summary: Complex networks are used to model many real-world systems.
Dimensionality reduction techniques like POD can be used in such cases.
We propose an algorithmic framework that combines techniques from pattern recognition and filtering theory.
- Score: 0.09324035015689712
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Complex networks are used to model many real-world systems. However, the
dimensionality 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. We
propose an algorithmic framework that combines techniques from pattern
recognition (PR) and stochastic filtering theory to enhance the output of such
models. The results of our study show that our method can improve the accuracy
of the surrogate model under perturbed inputs. Deep Neural Networks (DNNs) are
susceptible to adversarial attacks. However, recent research has revealed that
Neural Ordinary Differential Equations (neural ODEs) exhibit robustness in
specific applications. We benchmark our algorithmic framework with the neural
ODE-based approach as a reference.
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