The Deep Latent Space Particle Filter for Real-Time Data Assimilation with Uncertainty Quantification
This enables real-time data assimilation with uncertainty quantification for complex physical systems, such as leak localization in pipe flow and seabed identification for water waves, representing a strong domain-specific advancement.
The paper tackles the computational challenge of real-time data assimilation with uncertainty quantification by introducing the Deep Latent Space Particle Filter (D-LSPF), which runs orders of magnitude faster than high-fidelity methods and 3-5 times faster than alternatives while being up to an order of magnitude more accurate.
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 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 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 physical systems.