Surrogate models are of high interest for many engineering applications, serving as cheap-to-evaluate 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: https://github.com/SMTorg/smt-explainability .

翻译：代理模型在许多工程应用中备受关注，其作为黑箱函数的低成本、高效率近似，有助于工程师和从业者进行决策并理解复杂系统。因此，对可解释性方法的需求日益增长，已有大量研究致力于从代理模型中促进知识发现。为响应这些需求，本文介绍了SMT-EX——一个对开源Python代理建模工具箱（SMT）的增强版本，它将可解释性技术集成至先进的代理建模框架中。具体而言，SMT-EX包含三种关键的可解释性方法：沙普利加性解释、部分依赖图和个体条件期望。为此专门开发了SMT的可解释性依赖模块，该模块可在代理模型构建完成后轻松激活，为用户提供友好高效的工具以快速提取洞见。通过两个测试案例展示了SMT-EX的有效性：第一个案例是包含纯连续变量的10变量机翼重量问题，第二个是包含3变量混合分类属性的悬臂梁弯曲问题。基于SMT-EX对这些问题的分析，我们证明了其在处理多样化问题特征方面的通用性。SMT-Explainability已在Github上开源：https://github.com/SMTorg/smt-explainability。