Explainable AI for engineering design: A unified approach of systems engineering and component-based deep learning

Data-driven models created by machine learning gain in importance in all fields of design and engineering. They have high potential to assists 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. These drawbacks provide significant barriers retarding adoption in engineering design. 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 to systems engineering (SE). By means of the example of energy efficient building design, we first demonstrate better generalization of the component-based method by analyzing prediction accuracy outside the training data. Especially for representative designs different in structure, 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. In this way, the hierarchical component system forms a deep neural network (DNN) that a priori integrates information for engineering explainability. ...