Real-time bias-aware data assimilation

Open-source repository for advanced data assimilation techniques, focusing on bias correction in real-time thermoacoustic systems and beyond.

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🚀 Getting started

1. Prerequisites

• Anaconda or Miniconda installed.

2. Clone the Repository

git clone https://github.com/andreanovoa/real-time-bias-aware-DA
cd yourproject

3. Create and activate the Conda Environment

conda env create -f environment.yml
conda activate real-time-da

4. Install the package in editable mode

pip install -e . --use-pep517

5. (Optional) Run the tests and save the output onto a text file

pytest -s --log-cli-level=INFO test_tutorials.py >> test_output.txt 2>&1

Checkout the Tutorials folder, which includes several jupyter notebooks aiming to ease the understanding of the repository.

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🌟 What is available?

Data assimilation methods src.data_assimilation

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

Physical models src.models_physical

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

Data-driven models essentials.models_datadriven

• ESN_model -- Echo state network as a frorecasting tool
• POD-ESN -- Combines Proper Orthogonal Decomposition (POD) and ESN_model

Bias estimatorssrc.bias

• Echo State Network
• NoBias

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📂 Structure

.
├── data/              # (Generated) Data files
├── docs/              # Documents and media used in the notebooks
├── results/           # Any results generated will be stored here
├── scripts/           # Includes the files which run by themselves (e.g., main files, tutorials)
│   ├── mains/
│   ├── post_process/ 
│   └── tutorials/ 
├── src/               # Source code including all the objects, classes and functions required in scripts
│   ├── ML_models/
│   │   │── EchoStateNetwork.py
│   │   └── POD.py
│   ├── bias.py
│   ├── create.py   
│   ├── DA.py
│   ├── model.py
│   ├── models_datadriven.py
│   ├── models_physics.py
│   ├── plot_fns.py
│   ├── run.py
│   └── util.py
├─ tests_tutorials.py    # Unit tests
├─ environment.yml       # Conda environment definition
├─ setup.py              # Python package setup
└─ README.md             # This file

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📚 Main publications and presentations

Journal papers

• Nóvoa, Noiray, Dawson & Magri (2024). A real-time digital twin of azimuthal thermoacoustic instabilities. Journal of Fluid Mechanics. Published paper | 🏷️ v1.0.
• Nóvoa, Racca & Magri (2023). Inferring unknown unknowns. Computer Methods in Applied Mechanics and Engineering. Published paper | Legacy repository.
• Nóvoa & Magri (2022). Real-time thermoacoustic data assimilation. Journal of Fluid Mechanics. Published paper | Legacy repository.

Conference papers and proceedings

• Nóvoa & Magri (2025). Online model learning with data-assimilated reservoir computers. Preprint | 🏷️ v1.1.
• Nóvoa & Magri (2024). Real-time digital twins of multiphysics and turbulent flows. Paper.
• Nóvoa & Magri (2022). Bias-aware thermoacoustic data assimilation. In_ 51st International Congress and Exposition on Noise Control Engineering. Paper. | Legacy repository.

PhD theses

• Nóvoa (2024). Real-time data assimilation in nonlinear dynamcal systems. University of Cambridge. Thesis.

Conference presentations (incomplete list)

• APS-DFD 2024, Salt Lake City: Abstract | Poster.
• APS-DFD 2023, Washington DC: Abstract.
• EFMC14 2022, Athens: Abstract.
• APS-DFD 2022, Phoenix: Abstract.

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🤝 Contributing

Contributions, bug reports, and feature requests are welcome! Please open an issue or submit a pull request. For questions or collaborations, please reach out to A. Nóvoa.