Bayesian Inference for Fluid Dynamics: A Case Study for the Stochastic Rotating Shallow Water Model
Numerical Analysis
2022-01-03 v1 Numerical Analysis
Probability
Abstract
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 Stochastic Advection by Lie Transport (SALT) approach. The methodology we present here validates the applicability of tempering and sample regeneration via a Metropolis-Hastings algorithm to high-dimensional models used in stochastic fluid dynamics. The methodology is first tested on the Lorenz '63 model with both full and partial observations. Then we discuss the efficiency of the particle filter the SALT-SRSW model.
Cite
@article{arxiv.2112.15216,
title = {Bayesian Inference for Fluid Dynamics: A Case Study for the Stochastic Rotating Shallow Water Model},
author = {Peter Jan van Leeuwen and Dan Crisan and Oana Lang and Roland Potthast},
journal= {arXiv preprint arXiv:2112.15216},
year = {2022}
}
Comments
21 pages