1 citations · 1 across the 8 of their papers we have counts for
8 papers
Easy-to-Implement One-Step Schemes for Stochastic Integration
J. Woodfield, A. Lobbe
Convenient, easy to implement stochastic integration methods are developed on the basis of abstract one-step deterministic order integration techniques. The abstraction as an a…
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…
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…
Noise calibration for the stochastic rotating shallow water model
Dan Crisan, Oana Lang, Alexander Lobbe +2
Stochastic partial differential equations have been used in a variety of contexts to model the evolution of uncertain dynamical systems. In recent years, their applications to geop…
Comparison of Stochastic Parametrization Schemes using Data Assimilation on Triad Models
Bertrand Chapron, Dan Crisan, Darryl Holm +3
In recent years, stochastic parametrizations have been ubiquitous in modelling uncertainty in fluid dynamics models. One source of model uncertainty comes from the coarse graining…
Deep Learning for the Benes Filter
Alexander Lobbe
The Benes filter is a well-known continuous-time stochastic filtering model in one dimension that has the advantage of being explicitly solvable. From an evolution equation point o…
An application of the splitting-up method for the computation of a neural network representation for the solution for the filtering equations
Dan Crisan, Alexander Lobbe, Salvador Ortiz-Latorre
The filtering equations govern the evolution of the conditional distribution of a signal process given partial, and possibly noisy, observations arriving sequentially in time. Thei…
Pathwise approximations for the solution of the non-linear filtering problem
Dan Crisan, Alexander Lobbe, Salvador Ortiz-Latorre
We consider high order approximations of the solution of the stochastic filtering problem, derive their pathwise representation in the spirit of the earlier work of Clark and Davis…