[论文解读] Explainable AI for Engineering Design: A Unified Approach of Systems Engineering and Component- Based Deep Learning Demonstrated by Energy- Efficient Building Design

Philipp Geyer, Manav Mahan Singh+1 more|arXiv (Cornell University)|Aug 30, 2021

Probabilistic and Robust Engineering Design参考文献 33被引用 4

一句话总结

本文提出了一种基于组件的深度学习框架，将系统工程与可解释人工智能相结合，用于工程设计，通过可解释的接口激活提升模型的透明度和可重用性。在节能建筑的设计中，该方法实现了更优的泛化性能（R² = 0.94 vs. 0.71），并与物理仿真结果高度一致（围护结构R² = 0.92–0.99，区域R² = 0.78–0.93），通过敏感性分析和决策树实现了改进的可解释性。

ABSTRACT

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). The key contribution of the component-based, method 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. The, approach adapts the model structure to engineering methods of, systems engineering and to domain knowledge. We examine the, performance of the approach by the field of energy-efficient, building design: First, we observed 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).

研究动机与目标

• 解决传统机器学习模型在工程设计中存在黑箱特性及可重用性有限的问题。
• 提升模型在训练数据分布之外的泛化能力，尤其针对结构多样的设计方案。
• 将系统工程和物理仿真中的领域特定知识整合到深度学习架构中。
• 通过可解释的接口激活和基于规则的解释方法实现可解释性。
• 通过定量和定性的可解释性指标，在真实世界的节能建筑设计中验证该方法。

提出的方法

• 该方法将设计问题分解为分层的组件，每个组件均建模为独立的深度神经网络（DNN），并具有特定领域的输入和输出。
• 组件之间的接口激活被约束为表示可解释的工程量（例如，传热系数、区域温度）。
• 整个系统构成一个深度神经网络，其中组件间的连接反映了物理和系统工程关系。
• 模型结构基于系统工程原则构建，包括模块化、抽象化和接口标准化。
• 通过敏感性分析和基于激活模式训练的浅层决策树增强可解释性。
• 通过将预测结果与白盒仿真结果进行比较，使用R²相关性度量验证性能。

实验结果

研究问题

• RQ1 与整体式模型相比，基于组件的深度学习架构是否能在分布外的建筑设计方案上提升泛化性能？
• RQ2 组件模型中的接口激活在多大程度上与具有物理意义的工程量对齐？
• RQ3 敏感性分析和决策树在多大程度上能从模型内部表征中提取可解释的洞察？
• RQ4 该基于组件的模型在建筑围护结构和区域组件方面，与高保真白盒仿真结果的匹配程度如何？
• RQ5 是否可以利用领域知识和仿真基准来信任和验证模型的预测结果？

主要发现

• 基于组件的模型在预测结构不同的建筑能源性能方面取得了R² = 0.94的性能，显著优于整体式模型（R² = 0.71）。
• 围护结构组件的接口激活与仿真结果表现出强相关性（R² = 0.92–0.99），证实了其物理可解释性。
• 区域层级的预测也表现出高保真度，与仿真输出相比R²为0.78–0.93。
• 敏感性分析和决策树成功从模型激活中提取出与工程直觉一致的可解释规则。
• 该模型在新型和结构多样的设计配置上表现出更优的泛化能力。
• 将系统工程原则整合到深度学习架构中，从一开始就实现了高性能与可解释性的统一。