ronaalp

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

https://github.com/cscherding/ronaalp

Science Score: 44.0%

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Low similarity (8.1%) to scientific vocabulary

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Repository

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

Basic Info

Host: GitHub
Owner: cscherding
License: gpl-3.0
Language: HTML
Default Branch: master
Size: 3.98 MB

Statistics

Stars: 7
Watchers: 1
Forks: 4
Open Issues: 0
Releases: 0

Created almost 3 years ago · Last pushed over 2 years ago

Metadata Files

Readme License Citation

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.

Owner

Name: Clément Scherding
Login: cscherding
Kind: user

Repositories: 1
Profile: https://github.com/cscherding

Citation (CITATION.cff)

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: DyCors
message: 'If you use this software, please cite it as below.'
type: software
authors:
  - given-names: Clement
    family-names: Scherding
    email: >-
      clement.scherding@dalembert.upmc.fr
    affiliation: CNRS
license: GPL-3.0
version: 0.0.1
doi: 10.5281/zenodo.5907690

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