Prediction of core neutronic parameter based on neural network hyper parameter optimization method

<bold>Background</bold>Neural networks, with their powerful fitting capabilities, can learn the relationships between input and output variables based on large amounts of data, often serve as proxy models for physical programs in the field of engineering calculations, including nuclear engineering calculations. Neutron transport calculations, as one of the core links in neutronics simulations, are often suffer from lengthy computational times. However, this issue can also be addressed by utilizing neural network models. Nevertheless, neural network models have a series of hyperparameters that need to be set, but manually adjusting these hyperparameters is laborious, repetitive, and reliant only on experience. Moreover, these hyperparameters are not reusable when solving different problems.<bold>Purpose</bold>This study aims to propose a novel algorithm to improve computational efficiency of core neutronic parameter prediction, and provide reference for the application of artificial intelligence in core physics calculation.<bold>Methods</bold>The dataset was derived from the VITAS program's calculations of the TAKEDA1 and TAKEDA2 benchmark problems, consisting of 10 000 and 20 000 sets respectively. The core layout sequence was taken as input whilst the output included the effective multiplication factor and the regional integral flux. Thereafter, the core layout sequence was mapped into a one-dimensional vector, and the dataset was divided into training and validation sets in a 6:4 ratio. Then, the Bayesian optimization algorithm was employed to adjust fully connected neural network (FCNN) hyperparameters, and the adaptive learning rate decay and loss function optimization methods were combined to automatically search for the optimal combination of hyperparameters for datasets with different problems. Finally, critical core parameters, i.e., the effective multiplication factor and the regional integrated flux, were fitted to valid the generalization and accuracy of abovementioned algorithm, and a comparative experiment was conducted using manually set hyperparameters and hyperparameters output by Bayesian optimization as neural network training parameters.<bold>Results</bold>The results demonstrate that Bayesian optimization effectively enhances the neural network's accuracy. Specifically, the average error of the effective multiplication factor is within 1.50×10^-3, while that of the regional integral flux on the TAKEDA1 dataset is 1.72%, with a maximum error rate of 7.56%.<bold>Conclusions</bold>This approach can automatically search for the optimal combination of hyperparameters for different datasets to achieve the best performance, demonstrating high flexibility, efficiency, and strong generalization.