| Abstract: |
| Consider a complex system consisting of a large number of interacting agents, coupled through pairwise interactions with nonhomogeneous weights. Simulating the dynamics and identifying patterns of collective behavior can become computationally expensive, particularly as the system size grows, making most of the related algorithms unscalable. In this talk, I propose a model-reduction framework that transforms heterogeneous interacting particle systems into multi-community mean-field models by accounting for the network community structures for reduction. I will also introduce two structure-preserving numerical methods for solving these reduced mean-field equations. |
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