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We demonstrate the adaption of three established methods to the field ofsurrogate machine learning model development. These methods are dataaugmentation, custom loss functions and transfer learning. Each of thesemethods have seen widespread use in the field of machine learning, however,here we apply them specifically to surrogate machine learning modeldevelopment. The machine learning model that forms the basis behind this workwas intended to surrogate a traditional engineering model used in the UKnuclear industry. Previous performance of this model has been hampered by poorperformance due to limited training data. Here, we demonstrate that through acombination of additional techniques, model performance can be significantlyimproved. We show that each of the aforementioned techniques have utility intheir own right and in combination with one another. However, we see them bestapplied as part of a transfer learning operation. Five pre-trained surrogatemodels produced prior to this research were further trained with an augmenteddataset and with our custom loss function. Through the combination of all threetechniques, we see an improvement of at least $38\%$ in performance across thefive models.

Data-driven Approaches to Surrogate Machine Learning Model Development