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 a better performance and sustainability. However, limited generalization and the black-box nature of these models induce 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 generalization of the component-based method by accurately predicting the performance of designs with random structure different from training data. Second, we illustrate explainability by local sampling, sensitivity information and rules derived from low-depth decision trees and by evaluating this information from an engineering design perspective. The key for 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 directly integrates information for engineering explainability. 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 domain knowledge.
翻译:在设计和工程的所有领域,机械学习所创造的数据驱动模型在设计和工程的所有领域都具有重要性。它们具有协助决策者创造具有更好的性能和可持续性的新工艺品的巨大潜力。然而,这些模型的有限概括性和黑箱性质导致解释性和可重复性有限。这些缺陷为工程设计采用工程设计提供了重大障碍。为了克服这种情况,我们提议了一种基于组成部分的方法,通过机器学习(ML)来创建部分组成部分模型。这种基于组成部分的方法使深层次的学习与系统工程(SE)相一致。通过节能建筑设计的例子,我们首先通过精确预测与培训数据不同的随机结构的设计性能,来展示基于组成部分的方法的普及性。第二,我们用当地抽样、敏感性信息和规则来说明可解释性和可重复性,并从工程设计的角度来评价这一信息。为了克服这种情况,我们建议了一种基于组成部分的基于组成部分的基于组成部分的方法,即通过机器学习(MLL)创建部分构成一个可以解释的深度学习和系统工程设计(SEnoral)网络。我们首先通过精确的模型和可理解性工程结构结构的大规模配置配置,通过可理解性模型和可理解性模型的可理解性模型的可分析性模型,从而可以对数据进行新的分析性模型的精确性模型的分布进行新的分析。