A deterministic McKean-Pontryagin minimum principle is formulated for stochastic optimal control via auxiliary functions that enable Hamiltonian structure and time-decoupling.
Probabilistic Forecasting and Bayesian Data Assimilation
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SSLS combines score-based Langevin Monte Carlo with annealing for nonlinear posterior updates in sequential assimilation, supported by total-variation convergence bounds that establish asymptotic stability and numerical tests in high-dimensional nonlinear settings.
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On a mean-field Pontryagin minimum principle for stochastic optimal control
A deterministic McKean-Pontryagin minimum principle is formulated for stochastic optimal control via auxiliary functions that enable Hamiltonian structure and time-decoupling.
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Nonlinear Assimilation via Score-based Sequential Langevin Sampling
SSLS combines score-based Langevin Monte Carlo with annealing for nonlinear posterior updates in sequential assimilation, supported by total-variation convergence bounds that establish asymptotic stability and numerical tests in high-dimensional nonlinear settings.