Abstract
The application of machine learning in the nuclear field has been considered for the prediction of neutron irradiation embrittlement of reactor pressure vessel (RPV) steels in recent years. In this study, the RPV irradiation surveillance data are summarized and the integration of physical mechanisms with machine learning is investigated. It is found that the experimental results of the fusion model outperform the single machine learning models or physics formulas. In addition, the data amount of the RPV dataset is enhanced using the variational auto-encoder (VAE) model. Then a combined model of VAE and physical formula guided multilayer perceptron (VPMLP) is proposed, and its advantages in terms of prediction accuracy and generalization ability are experimentally demonstrated.
This work was supported by National Natural Science Foundation of China (No. 12205188), Natural Science Foundation of Shanghai (No. 22ZR1428700), and China National Nuclear Corporation (No. CNNC-LCKY-202236).
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Jin, P., Chen, L., Chen, H., Kong, L., Li, Z. (2024). Machine Learning-Driven Reactor Pressure Vessel Embrittlement Prediction Model. In: Liu, F., Sadanandan, A.A., Pham, D.N., Mursanto, P., Lukose, D. (eds) PRICAI 2023: Trends in Artificial Intelligence. PRICAI 2023. Lecture Notes in Computer Science(), vol 14325. Springer, Singapore. https://doi.org/10.1007/978-981-99-7019-3_9
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DOI: https://doi.org/10.1007/978-981-99-7019-3_9
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