ArticleJournal of nonlinear science2018
Stochastic Geometric Models with Non-stationary Spatial Correlations in Lagrangian Fluid Flows.
Article in Journal of nonlinear science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Sustainable development between sports facilities and ecological environment based on the dual carbon background.Scientific reports · 2024Article
- Well-posedness for a stochastic 2D Euler equation with transport noise.Stochastic partial differential equations : analysis and computations · 2023Article
- Stochastic variational principles for the collisional Vlasov-Maxwell and Vlasov-Poisson equations.Proceedings. Mathematical, physical, and engineering sciences · 2021Article
- Lie Group Cohomology and (Multi)Symplectic Integrators: New Geometric Tools for Lie Group Machine Learning Based on Souriau Geometric Statistical Mechanics.Entropy (Basel, Switzerland) · 2020Article
- Noise and Dissipation on Coadjoint Orbits.Journal of nonlinear science · 2018Article
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Authors and funding
2 authors.
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Abstract
Inspired by spatiotemporal observations from satellites of the trajectories of objects drifting near the surface of the ocean in the National Oceanic and Atmospheric Administration's "Global Drifter Program", this paper develops data-driven stochastic models of geophysical fluid dynamics (GFD) with non-stationary spatial correlations representing the dynamical behaviour of oceanic currents. Three models are considered. Model 1 from Holm (Proc R Soc A 471:20140963, 2015) is reviewed, in which the spatial correlations are time independent. Two new models, called Model 2 and Model 3, introduce two different symmetry breaking mechanisms by which the spatial correlations may be advected by the flow. These models are derived using reduction by symmetry of stochastic variational principles, leading to stochastic Hamiltonian systems, whose momentum maps, conservation laws and Lie-Poisson bracket structures are used in developing the new stochastic Hamiltonian models of GFD.
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