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            <funder>The work of Pramudita Satria Palar and Mohammad Daffa Robani was supported by Institut Teknologi Bandungthrough the International Research Collaboration for New and Renewable Energy Program. The work of Saves Paul andJoseph Morlier is part of the activities of ONERA - ISAE - ENAC joint research group. We thank Rémi Lafage forits constant help and for being the administrator of the SMTorg open-source organization on GitHub. The researchpresented in this paper has been performed in the framework of the COLOSSUS project (Collaborative System ofSystems Exploration of Aviation Products, Services and Business Models) and has received funding from the EuropeanUnion Horizon Europe program under grant agreement n◦ 101097120</funder>
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              <p>Surrogate models are of high interest for many engineering applications, serving as cheap-toevaluate time-efficient approximations of black-box functions to help engineers and practitioners make decisions and understand complex systems. As such, the need for explainability methods is rising and many studies have been performed to facilitate knowledge discovery from surrogate models. To respond to these enquiries, this paper introduces SMT-EX, an enhancement of the open-source Python Surrogate Modeling Toolbox (SMT) that integrates explainability techniques into a state-of-the-art surrogate modelling framework. More precisely, SMT-EX includes three key explainability methods: Shapley Additive Explanations, Partial Dependence Plot, and Individual Conditional Expectations. A peculiar explainability dependency of SMT has been developed for such purpose that can be easily activated once the surrogate model is built, offering a user-friendly and efficient tool for swift insight extraction. The effectiveness of SMT-EX is showcased through two test cases. The first case is a 10-variable wing weight problem with purely continuous variables and the second one is a 3-variable mixed-categorical cantilever beam bending problem. Relying on SMT-EX analyses for these problems, we demonstrate its versatility in addressing a diverse range of problem characteristics. SMT-Explainability is freely available on Github.</p>
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              <p>Les modèles de substitution suscitent un grand intérêt dans de nombreuses applications en ingénierie, car ils offrent des approximations rapides et peu coûteuses de fonctions complexes en "boîte noire". Ces modèles aident les ingénieurs et les praticiens à prendre des décisions et à mieux comprendre les systèmes complexes. Ainsi, le besoin de méthodes d'explicabilité ne cesse de croître, et de nombreuses études ont été menées pour faciliter l'extraction de connaissances à partir de modèles de substitution. Pour répondre à ces besoins, cet article présente SMT-EX, une amélioration de la boîte à outils open-source Python Surrogate Modeling Toolbox (SMT), qui intègre des techniques d'explicabilité dans un cadre de modélisation de substitution à la pointe de la technologie.Plus précisément, SMT-EX inclut trois méthodes clés d'explicabilité : les explications additives de Shapley (SHAP), les graphes de dépendance partielle (PDP) et les espérances conditionnelles individuelles (ICE). Une dépendance d'explicabilité spécifique à SMT a été développée à cet effet et peut être facilement activée une fois le modèle de substitution construit, offrant ainsi un outil intuitif et performant pour une extraction rapide des informations.L'efficacité de SMT-EX est illustrée à travers deux cas tests. Le premier cas est un problème de poids d'aile avec 10 variables purement continues, tandis que le second concerne un problème de flexion d'une poutre en porte-à-faux avec 3 variables mixtes catégoriques. En s'appuyant sur les analyses réalisées avec SMT-EX pour ces problèmes, nous démontrons sa polyvalence face à une grande diversité de caractéristiques de problèmes. SMT-EXplainability est disponible gratuitement sur GitHub.</p>
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