The Most Probable Transition Paths of Stochastic Dynamical Systems: Equivalent Description and Characterization

This work is devoted to show an equivalent description for the most probable transition paths of stochastic dynamical systems with Brownian noise, based on the theory of Markovian bridges. The most probable transition path for a stochastic dynamical system is the minimizer of the Onsager-Machlup action functional, and thus determined by the Euler-Lagrange equation (a second order differential equation with initial-terminal conditions) via a variational principle. After showing that the Onsager-Machlup action functional can be derived from a Markovian bridge process, we first demonstrate that, in some special cases, the most probable transition paths can be determined by first order deterministic differential equations with only a initial condition. Then we show that for general nonlinear stochastic systems with small noise, the most probable transition paths can be well approximated by solving a first order differential equation or an integro differential equation on a certain time interval. Finally, we illustrate our results with several examples.