عنوان انگلیسی مقاله:
Deep convolutional learning for general early design stage prediction models
ترجمه فارسی عنوان مقاله:
یادگیری همگرای عمیق برای مدل های پیش گویی مرحله اولیه طراحی
Sciencedirect - Elsevier - Advanced Engineering Informatics, 42 (2019) 100982: doi:10:1016/j:aei:2019:100982
Sundaravelpandian Singaravela,⁎, Johan Suykensb, Philipp Geyera
Designers rely on performance predictions to direct the design toward appropriate requirements. Machine
learning (ML) models exhibit the potential for rapid and accurate predictions. Developing conventional ML
models that can be generalized well in unseen design cases requires an effective feature engineering and selection.
Identifying generalizable features calls for good domain knowledge by the ML model developer.
Therefore, developing ML models for all design performance parameters with conventional ML will be a timeconsuming
and expensive process. Automation in terms of feature engineering and selection will accelerate the
use of ML models in design.
Deep learning models extract features from data, which aid in model generalization. In this study, we (1)
evaluate the deep learning model’s capability to predict the heating and cooling demand on unseen design cases
and (2) obtain an understanding of extracted features. Results indicate that deep learning model generalization is
similar to or better than that of a simple neural network with appropriate features. The reason for the satisfactory
generalization using the deep learning model is its ability to identify similar design options within the data
distribution. The results also indicate that deep learning models can filter out irrelevant features, reducing the
need for feature selection.
Keywords: Convolutional neural network | Energy predictions | Machine learning | Feature learning