A data-driven framework reduces particle-based transfer operators via concentration projection, geometric manifold, and finite-state discretization to reproduce clustering transitions and metastable states from simulation data.
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Numerical experiments on McKean-Vlasov equations with attractive kernels reveal alternating aggregation-diffusion regimes in transient clustering, with density peak height only partially coupled to energetic mechanisms.
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Data-driven Reduction of Transfer Operators for Particle Clustering Dynamics
A data-driven framework reduces particle-based transfer operators via concentration projection, geometric manifold, and finite-state discretization to reproduce clustering transitions and metastable states from simulation data.
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Energetic characterisation of transient clustering dynamics in aggregation-diffusion systems
Numerical experiments on McKean-Vlasov equations with attractive kernels reveal alternating aggregation-diffusion regimes in transient clustering, with density peak height only partially coupled to energetic mechanisms.