The deep latent space particle filter for real-time data assimilation with uncertainty quantification

The deep latent space particle filter for real-time data assimilation with uncertainty quantification

Blog Article

Abstract In data assimilation, observations are fused with simulations to obtain an accurate estimate of the state and parameters for a given physical system.Combining data with a model, however, while accurately estimating uncertainty, is computationally expensive and infeasible to run in real-time for complex systems.Here, we present a novel particle filter methodology, the Deep Latent Space Particle filter or D-LSPF, that uses neural network-based surrogate models to Display Console Assembly overcome this computational challenge.The D-LSPF enables filtering in the low-dimensional latent space obtained using Wasserstein AEs with modified vision transformer layers for dimensionality reduction and transformers for parameterized latent space time stepping.As we demonstrate L-Tryptophan on three test cases, including leak localization in multi-phase pipe flow and seabed identification for fully nonlinear water waves, the D-LSPF runs orders of magnitude faster than a high-fidelity particle filter and 3-5 times faster than alternative methods while being up to an order of magnitude more accurate.

The D-LSPF thus enables real-time data assimilation with uncertainty quantification for the test cases demonstrated in this paper.