The goal of this work was to investigate the feasibility of developing machine learning models for predicting the values of aircraft configuration design variables when provided with time series of mission-informed performance parameters. Regression artificial neural networks, along with their associated training data, have been generated and tested for aircraft design space exploration scenarios. The bounds of the data used to train the models were partially informed by the configuration characteristics of the Boeing 737 Next Generation family. The effects of varying neural network architecture, along with the application of different data filtering schemes, on the models’ predictive accuracy have been examined. The results obtained demonstrated that cascade-forward shallow neural networks not only exhibited excellent generalization across the design space for which the model was calibrated for, but also showcased versatility when tasked with predicting design variable values for a configuration layout relatively different from the ones used for training. Furthermore, these models had favorable metrics in computational wall-clock time required and number of epochs needed for training.

这项工作的目的是研究开发机器学习模型的可行性，以便在提供任务通知性能参数的时间序列时预测飞机配置设计变量的值。回归人工神经网络及其相关的训练数据已针对飞机设计空间探索场景生成和测试。用于训练模型的数据范围部分来自波音 737 下一代系列的配置特征。已经检查了不同的神经网络架构以及不同数据过滤方案的应用对模型预测准确性的影响。获得的结果表明，级联前向浅层神经网络不仅在模型校准的设计空间中表现出出色的泛化能力，而且在预测配置布局的设计变量值的任务与用于的配置布局相对不同时展示了多功能性。训练。此外，这些模型在所需的计算挂钟时间和训练所需的时期数方面具有有利的指标。