73 citations · 102 across the 12 of their papers we have counts for
3 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…
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…
Bayesian Inference for Fluid Dynamics: A Case Study for the Stochastic Rotating Shallow Water Model
Peter Jan van Leeuwen, Dan Crisan, Oana Lang +1
In this work, we use a tempering-based adaptive particle filter to infer from a partially observed stochastic rotating shallow water (SRSW) model which has been derived using the S…