A pipeline combining importance sampling with Markov state models, chain-rule sensitivities, and RiteWeight reweighting enables efficient parameter optimization for rare-event dynamics in nonequilibrium systems.
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cond-mat.stat-mech 2years
2026 2verdicts
UNVERDICTED 2roles
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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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Sensitivity Analysis in the Face of Rare Events
A pipeline combining importance sampling with Markov state models, chain-rule sensitivities, and RiteWeight reweighting enables efficient parameter optimization for rare-event dynamics in nonequilibrium systems.
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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.