/smt-sgp-paper

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smt-sgp-paper

Repo related to the paper 'A Python Toolbox for Data-Driven Aerodynamic Modeling using Sparse Gaussian Processes'

Citation

Valayer, H.; Bartoli, N.; Castaño-Aguirre, M.; Lafage, R.; Lefebvre, T.; López-Lopera, A.F.; Mouton, S. A Python Toolbox for Data-Driven Aerodynamic Modeling Using Sparse Gaussian Processes. Aerospace 2024, 11, 260. https://doi.org/10.3390/aerospace11040260

SMT version

SMT 2.3.0 was used to get the results.

pip install smt==2.3.0

Repository Content

  • Notebooks

    • Analytic test case : sparse_gp_analytic.ipynb
    • Wind Tunnel test case: sparse_gp_wtdata.ipynb (Warning: wind tunnel data are not publicly available)

  • FIG_ANALYTIC directory contains figures generated from analytic test case notebook

  • FIG_WT directory contains figures generated from wind tunnel test case notebook

  • wtdata_results directory contains csv results generated by following scripts from wind tunnel data

  • sparse_gp_wtdata.py generates sgp_wtdata_results_M\<value\>.csv and deals with all output variable, all sparse methods, all inducing methods for a given M nb of inducing points

  • sparse_gp_wtdata_czc_nkmeans.py generates sgp_wtdata_results_czc_nkmeans.csv and deals with the CZC output variable, normalized kmeans and various M values

  • sparse_gp_wtdata_noseed_10times.py generates sgp_wtdata_results_noseed_M50_10times.csv and deals with all output variable, all sparse methods, all inducing methods by repeating ten times with no random seed

SMT Sparse GP tutorial

See SGP analytic notebook

Open In Colab