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Deterministic particle flows for constraining stochastic nonlinear systems
[article]
2021
arXiv
pre-print
Devising optimal interventions for constraining stochastic systems is a challenging endeavour that has to confront the interplay between randomness and nonlinearity. Existing methods for identifying the necessary dynamical adjustments resort either to space discretising solutions of ensuing partial differential equations, or to iterative stochastic path sampling schemes. Yet, both approaches become computationally demanding for increasing system dimension. Here, we propose a generally
arXiv:2112.05735v1
fatcat:syidouw7yzdejj27pboif4nofa