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The deep latent space particle filter for real-time data assimilation with uncertainty quantification.
Nikolaj T Mücke1,2, Sander M Bohté3,4, Cornelis W Oosterlee5
1Scientific Computing, Centrum Wiskunde & Informatica, 1098 XG, Amsterdam, The Netherlands. nikolaj.mucke@cwi.nl.
Scientific Reports
|August 21, 2024
Summary
A new Deep Latent Space Particle Filter (D-LSPF) uses neural networks to speed up data assimilation for complex systems. This method achieves faster and more accurate real-time estimations with uncertainty quantification.
Area of Science:
- Computational Science
- Data Assimilation
- Machine Learning
Background:
- Data assimilation combines observations with simulations for accurate system estimation.
- Current methods are computationally expensive, hindering real-time application in complex systems.
- Accurate uncertainty quantification remains a significant challenge.
Purpose of the Study:
- Introduce a novel particle filter methodology, the Deep Latent Space Particle Filter (D-LSPF).
- Overcome computational limitations of traditional data assimilation techniques.
- Enable real-time data assimilation with uncertainty quantification for complex physical systems.
Main Methods:
- Developed the Deep Latent Space Particle Filter (D-LSPF) utilizing neural network-based surrogate models.
- Employed Wasserstein Autoencoders (AEs) with vision transformer layers for dimensionality reduction.
- Utilized transformers for parameterized latent space time stepping.
Main Results:
- D-LSPF demonstrated significant speed improvements, running orders of magnitude faster than high-fidelity particle filters.
- Achieved 3-5 times faster performance compared to alternative methods.
- Showcased up to an order of magnitude improvement in accuracy.
- Successfully applied to leak localization in multi-phase pipe flow and seabed identification.
Conclusions:
- The D-LSPF effectively addresses the computational cost of data assimilation.
- Enables real-time data assimilation with uncertainty quantification for tested complex systems.
- Represents a significant advancement in applying data assimilation to real-world problems.
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