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            <funder>The research presented in this paper has been performed in the framework of the AGILE 4.0 project (Towards cyber-physical collaborative aircraft development), funded by the European Union Horizon 2020 research and innovation framework programme under grant agreement n 815122 and in the COLOSSUS project (Collaborative System of Systems Exploration of Aviation Products, Services and Business Models) funded by the European Union Horizon Europe research and innovation framework programme under grant agreement n 101097120.</funder>
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              <p>The Surrogate Modeling Toolbox (SMT) is an open-source Python package that offers a collection of surrogate modeling methods, sampling techniques, and a set of sample problems. This paper presents SMT 2.0, a major new release of SMT that introduces significant upgrades and new features to the toolbox. This release adds the capability to handle mixed-variable surrogate models and hierarchical variables. These types of variables are becoming increasingly important in several surrogate modeling applications. SMT 2.0 also improves SMT by extending sampling methods, adding new surrogate models, and computing variance and kernel derivatives for Kriging. This release also includes new functions to handle noisy and use multi-fidelity data. To the best of our knowledge, SMT 2.0 is the first open-source surrogate library to propose surrogate models for hierarchical and mixed inputs. This open-source software is distributed under the New BSD license. https://github.com/SMTorg/SMT.</p>
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              <p>La boîte à outils de modélisation par modèles de substitution (SMT) est un paquetage Python open-source qui offre une collection de méthodes de modélisation des substituts, des techniques d'échantillonnage et un ensemble d'exemples de problèmes. Cet article présente SMT 2.0, une nouvelle version majeure de SMT qui introduit des améliorations significatives et de nouvelles fonctionnalités à la boîte à outils. Cette version ajoute la possibilité de traiter des modèles de substitution à variables mixtes et des variables hiérarchiques. Ces types de variables deviennent de plus en plus importants dans plusieurs applications de modèles de substitution. SMT 2.0 améliore également SMT en étendant les méthodes d'échantillonnage, en ajoutant de nouveaux modèles de substitution et en calculant la variance et les dérivés du noyau pour le Krigeage. Cette version comprend également de nouvelles fonctions pour traiter les données bruyantes et utiliser des données multi-fidélité. À notre connaissance, SMT 2.0 est la première bibliothèque de substitution à code source ouvert à proposer des modèles de substitution pour des entrées hiérarchiques et mixtes. Ce logiciel libre est distribué sous la nouvelle licence BSD. https://github.com/SMTorg/SMT.</p>
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