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Long-time propagation of chaos and exit times for metastable mean-field particle systems
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Systems of stochastic particles evolving in a multi-well energy landscape and attracted to their barycenter is the prototypical example of mean-field process undergoing phase transitions: at low temperature, the corresponding mean-field deterministic limit has several stationary solutions, and the empirical measure of the particle system is then expected to be a metastable process in the space of probability measures, exhibiting rare transitions between the vicinity of these stationary solutions. We show two results in this direction: first, the exit time from such metastable domains occurs at time exponentially large with the number of particles and follows approximately an exponential distribution; second, up to the expected exit time, the joint law of particles remain close to the law of independent non-linear McKean-Vlasov processes.
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Cited by 2 Pith papers
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Continuity and Discontinuity of McKean-Vlasov Phase Transitions via Bifurcation Theory
Continuous and discontinuous McKean–Vlasov phase transitions in R^n are characterized by pitchfork and saddle-node bifurcations, with Dawson’s criterion extended via eigenvalues of the critical covariance matrix.
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Generating Samples of Stationary Distributions of Weakly Interacting Diffusion Models Without Finite Particle Truncation: A Weak Generative Approach
A generative neural sampler solves the nonlinear stationary Fokker-Planck equation of McKean-Vlasov processes and produces i.i.d. samples, capturing all metastable branches without finite-particle simulation.
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