The Geometry of most probable trajectories in noise-driven dynamical systems
Publication
, Conference
Neu, JC; Ghanta, A; Teitsworth, SW
Published in: Springer Proceedings in Mathematics and Statistics
January 1, 2018
This paper presents a heuristic derivation of a geometric minimum action method that can be used to determine most-probable transition paths in noise-driven dynamical systems. Particular attention is focused on systems that violate detailed balance, and the role of the stochastic vorticity tensor is emphasized. The general method is explored through a detailed study of a two-dimensional quadratic shear flow which exhibits bifurcating most-probable transition pathways.
Duke Scholars
Published In
Springer Proceedings in Mathematics and Statistics
DOI
EISSN
2194-1017
ISSN
2194-1009
ISBN
9783319765983
Publication Date
January 1, 2018
Volume
232
Start / End Page
153 / 167
Citation
APA
Chicago
ICMJE
MLA
NLM
Neu, J. C., Ghanta, A., & Teitsworth, S. W. (2018). The Geometry of most probable trajectories in noise-driven dynamical systems. In Springer Proceedings in Mathematics and Statistics (Vol. 232, pp. 153–167). https://doi.org/10.1007/978-3-319-76599-0_9
Neu, J. C., A. Ghanta, and S. W. Teitsworth. “The Geometry of most probable trajectories in noise-driven dynamical systems.” In Springer Proceedings in Mathematics and Statistics, 232:153–67, 2018. https://doi.org/10.1007/978-3-319-76599-0_9.
Neu JC, Ghanta A, Teitsworth SW. The Geometry of most probable trajectories in noise-driven dynamical systems. In: Springer Proceedings in Mathematics and Statistics. 2018. p. 153–67.
Neu, J. C., et al. “The Geometry of most probable trajectories in noise-driven dynamical systems.” Springer Proceedings in Mathematics and Statistics, vol. 232, 2018, pp. 153–67. Scopus, doi:10.1007/978-3-319-76599-0_9.
Neu JC, Ghanta A, Teitsworth SW. The Geometry of most probable trajectories in noise-driven dynamical systems. Springer Proceedings in Mathematics and Statistics. 2018. p. 153–167.
Published In
Springer Proceedings in Mathematics and Statistics
DOI
EISSN
2194-1017
ISSN
2194-1009
ISBN
9783319765983
Publication Date
January 1, 2018
Volume
232
Start / End Page
153 / 167