5 papers · 1 filter
Bayesian inference for geophysical fluid dynamics using generative models
Alexander Lobbe, Dan Crisan, Oana Lang
Data assimilation plays a crucial role in numerical modeling, enabling the integration of real-world observations into mathematical models to enhance the accuracy and predictive ca…
A uniform point vortex approximation for the solution of the two-dimensional Navier Stokes equation with transport noise
Filippo Giovagnini, Dan Crisan
We study a model of interacting particles represented by a system of N stochastic differential equations. We establish that the mollified empirical distribution of the system conve…
Generative Modelling of Stochastic Rotating Shallow Water Noise
Dan Crisan, Oana Lang, Alexander Lobbe
In recent work, the authors have developed a generic methodology for calibrating the noise in fluid dynamics stochastic partial differential equations where the stochasticity was i…
Global solutions for stochastically controlled fluid dynamics models
Dan Crisan, Oana Lang
For a class of evolution equations that possibly have only local solutions, we introduce a stochastic component that ensures that the solutions of the corresponding stochastically…
Data assimilation for the stochastic Camassa-Holm equation using particle filtering: a numerical investigation
Colin John Cotter, Dan Crisan, Maneesh Kumar Singh
In this study, we explore data assimilation for the Stochastic Camassa-Holm equation through the application of the particle filtering framework. Specifically, our approach integra…