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Reduced-Order Nonlinear Approximation with on-the-fly Learning Procedure

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RONAALP

Reduced Order Nonlinear Approximation with Active Learning Procedure

This repository contains the source code for the RONALP algorithm implementation in Python.

Examples for model training and update can be tested in the jupyter notebook in /example.

Data can be requested directly to the author upon reasonable request

Installation instructions

$ git clone https://github.com/cscherding/RONAALP.git
$ cd RONAALP
$ python setup.py install

Reference documentation

Reference documentation available in RONAALP docs.

References

[1] Scherding, C., Rigas, G., Sipp, D., Schmid, P. J., & Sayadi, T. (2023). Data-driven framework for input/output lookup tables reduction: Application to hypersonic flows in chemical nonequilibrium. Physical Review Fluids, 8(2), 023201.

[2] Scherding, C. (2023). Predictive modeling of hypersonic flows in thermochemical nonequilibrium: physics-based and data-driven approaches. PhD Thesis, Sorbone University.

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