Adapting Data-Driven Techniques to Improve Surrogate Machine Learning Model Performance

Abstract

We demonstrate the adaption of three established methods to the field of surrogate machine learning model development. These methods are data augmentation, custom loss functions and fine-tuning of pre-trained models. Each of these methods have seen widespread use in the field of machine learning, however, here we apply them specifically to surrogate machine learning model development. The machine learning model that forms the basis behind this work was intended to surrogate a traditional engineering model used in the UK nuclear industry. Previous performance of this model has been hampered by poor performance due to limited training data. Here, we demonstrate that through a combination of additional techniques, model performance can be significantly improved. We show that each of the aforementioned techniques have utility in their own right and in combination with one another. However, we see them best applied when used to fine-tune existing models. Five pre-trained surrogate models produced prior to this study were further trained using an augmented dataset and with our custom loss function. Through the combination of all three techniques, we see an improvement of at least 38% in performance across the five models.

Keywords

• Advanced gas-cooled reactor
• convolutional neural network
• data analysis
• data augmentation
• data science
• graphite