Data-driven models created by machine learning gain in importance in all fields of design and engineering. They have high potential to assist decision-makers in creating novel artefacts with better performance and sustainability. However, limited generalization and the black-box nature of these models lead to limited explainability and reusability. To overcome this situation, we propose a component-based approach to create partial component models by machine learning (ML). This component-based approach aligns deep learning with systems engineering (SE). For the domain of energy efficient building design, we first demonstrate better generalization of the component-based method by analyzing prediction accuracy outside the training data. We observe a much higher accuracy (R2 = 0.94) compared to conventional monolithic methods (R2 = 0.71). Second, we illustrate explainability by exemplary demonstrating how sensitivity information from SE and rules from low-depth decision trees serve engineering. Third, we evaluate explainability by qualitative and quantitative methods demonstrating the matching of preliminary knowledge and data-driven derived strategies and show correctness of activations at component interfaces compared to white-box simulation results (envelope components: R2 = 0.92..0.99; zones: R2 = 0.78..0.93). The key for component-based explainability is that activations at interfaces between the components are interpretable engineering quantities. The large range of possible configurations in composing components allows the examination of novel unseen design cases with understandable data-driven models. The matching of parameter ranges of components by similar probability distribution produces reusable, well-generalizing, and trustworthy models. The approach adapts the model structure to engineering methods of systems engineering and to domain knowledge.

翻译：机器学习驱动的数据模型在设计与工程各领域日益重要，这些模型在辅助决策者创造性能更优、可持续性更强的新颖造物方面潜力巨大。然而，模型的泛化能力局限与黑箱特性导致其可解释性与可重用性受限。为解决该问题，我们提出一种基于组件的方法，通过机器学习创建部分组件模型。该组件化方法将深度学习与系统工程相统一。以节能建筑设计领域为例，我们首先通过分析训练数据之外的预测精度，证明组件化方法具有更优泛化能力：本方法精度（R² = 0.94）显著高于传统整体方法（R² = 0.71）。其次，通过实例演示系统工程敏感性信息与低深度决策树规则如何服务于工程实践，阐释了可解释性。第三，采用定性与定量评估方法，验证先验知识与数据驱动策略的匹配度，并证明组件接口处激活值与白盒仿真结果的吻合度（围护结构组件：R²=0.92-0.99；分区组件：R²=0.78-0.93）。组件间接口激活量作为可解释的工程物理量，是组件级可解释性的关键。通过组合组件实现的大范围配置空间，使得可用数据驱动模型探索未见的新颖设计案例。基于相似概率分布匹配组件参数范围，可生成可重用、强泛化且可信赖的模型。该方法使模型结构自适应于系统工程方法论与领域知识体系。