%0 Conference Paper %F Oral %T A general square exponential kernel to handle mixed-categorical variables for Gaussian process %+ Institut Supérieur de l'Aéronautique et de l'Espace (ISAE-SUPAERO) %+ ONERA / DTIS, Université de Toulouse [Toulouse] %A Saves, Paul %A Diouane, Youssef %A Bartoli, Nathalie %A Lefebvre, Thierry %A Morlier, Joseph %< avec comité de lecture %3 AIAA AVIATION 2022 Forum %B AIAA AVIATION 2022 Forum %C Chicago (virtual), France %I American Institute of Aeronautics and Astronautics %8 2022-06-27 %D 2022 %R 10.2514/6.2022-3870 %K PROCESSUS GAUSSIEN, NOYAU CORRELATION MIXTE %Z Computer Science [cs] %Z Statistics [stat] %Z Mathematics [math] %Z Engineering Sciences [physics]Conference papers %X Recently, there has been a growing interest for mixed categorical meta-models based on Gaussian process (GP) surrogates. In this setting, several existing approaches use different strategies. Among the recently developed methods, we could cite: GP models built using continuous relaxation of the variables, Gower distance based models or GP models derived from direct estimation of the correlation matrix. In this paper, we present a kernel-based approach that extends continuous Gaussian kernels to handle mixed-categorical variables. The proposed kernel leads to a GP surrogate that generalizes continuous relaxation and Gower distance based GP models. The good potential of the proposed framework is shown on analytical mixed-categorical variables test cases. On different settings, our proposed GP models is as accurate as the state-of-the-art GP models. %G English %2 https://hal.science/hal-03700850/document %2 https://hal.science/hal-03700850/file/Aviation_final_paul%289%29.pdf %L hal-03700850 %U https://hal.science/hal-03700850 %~ ONERA %~ INSMI %~ ONERA-MIP %~ FONISEN %~ FONISEN-X-OAD %~ FONISEN-MA2N