Dynamical systems arising in biophysics are often subject to random fluctuations. The noisy fluctuations may be Gaussian or non-Gaussian, which are modeled by Brownian motion or α-stable Levy motion, respectively. Non-Gaussianity of the noise manifests as nonlocality at a “macroscopic” level. Stochastic dynamical systems with non-Gaussian noise (modeled by α-stable Levy motion) have attracted a lot of attention recently. The non-Gaussianity index α is a significant indicator for various dynamical behaviors.

Transition phenomena are special events for evolution from one metastable state to another in stochastic dynamical systems, caused by the interaction between nonlinearity and uncertainty. Examples for such events are phase transition, pattern change, gene transcription, climate change, abrupt change, extreme transition, and other rare events. The most probable transition pathways are the maximal likely (in the sense of optimizing a probability or an action functional) trajectory between metastable states.

The speaker will present recent work on analyzing and computing the most probable transition pathways for stochastic dynamical systems, in the context of the Onsager-Machlup action functionals.

会议网址：https://zoom.us/j/5066356571?pwd=enlBUmlSNjZSeXhreWpWcjZUUFhjdz09

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