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Local manifold learning and its link to domain-based physics knowledge

This repository contains code and materials to reproduce the results from the "Local manifold learning and its link to domain-based physics knowledge" paper.

K. Zdybał, G. D'Alessio, A. Attili, A. Coussement, J. C. Sutherland, A. Parente - Local manifold learning and its link to domain-based physics knowledge, 2023, Applications in Energy and Combustion Science

You can find the open-source article here: https://www.sciencedirect.com/sdfe/reader/pii/S2666352X23000201/pdf.

BibTeX citation:

@article{zdybal2023local,
  title={Local manifold learning and its link to domain-based physics knowledge},
  author={Zdybał, Kamila and D'Alessio, Giuseppe and Attili, Antonio and Coussement, Axel and Sutherland, James C and Parente, Alessandro},
  journal={Applications in Energy and Combustion Science},
  volume={Special issue: Machine Learning Methods for Reactive Flows},
  pages = {100131},
  issn = {2666-352X},
  year={2023},
  publisher={Elsevier}
}

Our hypothesis

Local PCA can find the intrinsic low-dimensional parameterization of combustion systems in a data-driven way.

Our methodology

Data availability

All datasets used in the current work are provided in the data directory. The datasets have been generated with the open-source Spifire Python library.

Reproducing paper results using Jupyter notebooks

All code used to produce the results in the original publication and in the supplementary material can be found in the Jupyter notebooks provided in the code directory. PCAfold library is required.

Below, are the detailed guidelines on reproducing each figure from the original publication:

📄 $\S4.1$ The Burke-Schumann model

This Jupyter notebook can be used to reproduce results in $\S4.1$.

📄 $\S4.2$ The chemical equilibrium model

This Jupyter notebook can be used to reproduce results in $\S4.2$.

📄 $\S4.3$ The homogeneous reactor model

This Jupyter notebook can be used to reproduce results in $\S4.3$.

kamilazdybal/local-manifold-learning