Bias-aware data assimilation

This work was developed during the PhD of A. Nóvoa. The thesis is publicly avaiable here.

Main publications and presentations

Journal papers

• A real-time digital twin of azimuthal thermoacoustic instabilities. Journal of Fluid Mechanics (To appear)
• Inferring unknown unknowns. Computer Methods in Applied Mechanics and Engineering (2023).
  • The legacy repository used for this paper can be found in MagriLab/rBA-EnKF
• Real-time thermoacoustic data assimilation. Journal of Fluid Mechanics (2022).
  • The legacy repository used for this paper can be found in MagriLab/Real-time-TA-DA

Conference papers

• Bias-aware thermoacoustic data assimilation. In 51st International Congress and Exposition on Noise Control Engineering (2022).
  • The legacy repository used for this paper can be found in MagriLab/IN22-Bias-aware-TADA

Conference presentations (incomplete)

• APS-DFD 2024 Salt Lake City Abstract and interactive flash talk poster.
• APS-DFD 2023 Washington DC Abstract.
• EFMC14 2022 Athens Abstract
• APS-DFD 2022 Phoenix Abstract.

Getting started

1. Install the requirements here.
2. Checkout the Tutorials folder, which includes several jupyter notebooks aiming to ease the understanding of the repository.

What is available?

Data assimilation methods:

• EnKF - ensemble Kalman filter
• EnSRKF - ensemble square-root Kalman filter
• rBA-EnKF - regularized bias-aware EnKF

Physical models:

• Rijke tube model (dimensional with Galerkin projection)
• Van der Pols
• Lorenz63
• Azimuthal thermoaocustics model

Bias estimators:

• Echo State Network
• NoBias