ABSTRACT In parametric design environments, the use of Artificial Neural Networks (ANNs) promises greater feasibility than simulations in exploring the performance of solution spaces due to a reduction in overall computation time. This is because ANNs, once trained on selected input and output patterns, enable instantaneous predictions for new unseen input. In this study, ANNs were trained on simulation data to learn the relationship between design parameters and the resulting daylight performance. The ANNs were trained with selected input-output patterns generated from a reduced set of simulations in order to predict daylight performance for a hypercube of design solutions. This work demonstrates the integration of ANNs in a case study exploring designs for the central atrium of a school building. The study discusses the obtained design results and highlights the efficacy of the proposed method. Conclusions are drawn on the advantages of brute-force based daylight design explorations and an ANN-integrated design approach.

中文翻译：

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用于参数化日光设计的人工神经网络

摘要在参数化设计环境中，由于总体计算时间的减少，人工神经网络 (ANN) 的使用在探索解决方案空间的性能方面比模拟具有更大的可行性。这是因为 ANN 一旦对选定的输入和输出模式进行了训练，就可以对新的看不见的输入进行即时预测。在这项研究中，人工神经网络接受了模拟数据的训练，以了解设计参数与产生的日光性能之间的关系。人工神经网络使用从一组简化的模拟中生成的选定输入-输出模式进行训练，以预测超立方体设计解决方案的日光性能。这项工作展示了 ANN 在探索学校建筑中庭设计的案例研究中的集成。该研究讨论了获得的设计结果，并强调了所提出方法的有效性。结论是基于基于蛮力的日光设计探索和 ANN 集成设计方法的优势。