Onsager-Machlup action functional for stochastic partial differential equations with Levy noise

Abstract

This work is devoted to deriving the Onsager-Machlup action functional for stochastic partial differential equations with (non-Gaussian) Levy process as well as Gaussian Brownian motion. This is achieved by applying the Girsanov transformation for probability measures and then by a path representation. This enables the investigation of the most probable transition path for infinite dimensional stochastic dynamical systems modeled by stochastic partial differential equations, by minimizing the Onsager-Machlup action functional.

References in corpus (6)

• Effective action for stochastic partial differential equations
• The Onsager-Machlup Function as Lagrangian for the Most Probable Path of a Jump-diffusion Process
• A Machine Learning Framework for Computing the Most Probable Paths of Stochastic Dynamical Systems
• Stochastic analysis in a tubular neighborhood or Onsager-Machlup functions revisited
• Estimating the Most Probable Transition Time for Stochastic Dynamical Systems
• On the path-independence of the Girsanov transformation for stochastic evolution equations with jumps in Hilbert spaces

Cited by in corpus (2)

• Lévy-noise versus Gaussian-noise-induced Transitions in the Ghil-Sellers Energy Balance Model
• Transition pathways for a class of high dimensional stochastic dynamical systems with Lévy noise