Linyu Lin

Wang, L., Lin, L., & Dinh, N. (2023). Data coverage assessment on neural network based digital twins for autonomous control system. ANNALS OF NUCLEAR ENERGY, 182. https://doi.org/10.1016/j.anucene.2022.109568

Lin, L., Athe, P., Rouxelin, P., Avramova, M., Gupta, A., Youngblood, R., … Dinh, N. (2022). Digital-twin-based improvements to diagnosis, prognosis, strategy assessment, and discrepancy checking in a nearly autonomous management and control system. ANNALS OF NUCLEAR ENERGY, 166. https://doi.org/10.1016/j.anucene.2021.108715

Lee, J., Lin, L., Athe, P., & Dinh, N. (2021). Development of the Machine Learning-based Safety Significant Factor Inference Model for Diagnosis in Autonomous Control System. ANNALS OF NUCLEAR ENERGY, 162. https://doi.org/10.1016/j.anucene.2021.108443

Lin, L., Lee, J., Poudel, B., McJunkin, T., Dinh, N., & Agarwal, V. (2021). Enhancing the Operational Resilience of Advanced Reactors with Digital Twins by Recurrent Neural Networks. 2021 RESILIENCE WEEK (RWS). https://doi.org/10.1109/RWS52686.2021.9611796

Lin, L., Bao, H., & Dinh, N. (2021). [Review of Uncertainty quantification and software risk analysis for digital twins in the nearly autonomous management and control systems: A review]. ANNALS OF NUCLEAR ENERGY, 160. https://doi.org/10.1016/j.anucene.2021.108362