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1. Argaud, J.P; Bouriquet, B; Gong, H.*; Maday,Y; Mula,O. Sensor placement in nuclear reactors based on the Generalized Empirical Interpolation Method. Journal of Computational Physics, 2018, 363: 354-370. [SCI]
2. Shi, B., Qiu, Q., Gong, H., Li, Q., & Luo, Y. (2023, August). Noninvasive Data-Driven Prediction of Reactor Power Field: An MLP-Based Approach. In 2023 IEEE Smart World Congress (SWC) (pp. 1-6). IEEE.
3. Li, H., Lu, J., Ji, H., Hong, L., & Gong, H. (2023). A noise and vibration tolerant resnet for field reconstruction with sparse sensor. Communications in Computational Physics.
4. Jianpeng Liu, Zhiyong Wang, Qing Li. & Helin Gong*.A Hybrid Data Assimilation and Dynamic Mode Decomposition Approach for Xenon Dynamic Prediction of Nuclear Reactor Cores.Nuclear Science and Engineering, Taylor & Francis, 2024, 0, 1-19.
5. P. Zhang, H. Gong and K. Wang. Preliminary Study on Neutronics Characteristics of Thorium-based Supercritical Water-cooled Fast Reactor. The 5th Int. Sym. SCWR (ISSCWR-5). Vancouver, British Columbia, Canada, March 13-16, 2011.
6. H. Gong, J. P. Argaud, B. Bouriquet, and Y. Maday. The Empirical Interpolation Method applied to the neutron diffusion equations with parameter dependence. In Proceedings of PHYSOR 2016.
7. J.P. Argaud, B. Bouriquet, H. Gong, Y. Maday and O. Mula. Stabilization of (G)EIM in Presence of Measurement Noise: Application to Nuclear Reactor Physics. In Marco L. Bittencourt, Ney A. Dumont, and Jan S. Hesthaven, editors, Spectral and High Order Methods for Partial Differential Equations ICOSAHOM 2016, pages 133-145, Cham, 2017. Springer International Publishing.
8. H. Gong, J.P. Argaud, B. Bouriquet, Y. Maday and O. Mula. Monitoring flux and power in nuclear reactors with data assimilation and reduced models. In Proceedings of M&C 2017.
9. H. Gong, Q. Li, Y. Yu, and Y. Maday. “EIM in the frame of least-squares optimal interpolation method.” Annual meeting of Science and Technology on Reactor System Design Technology Laboratory. Chengdu, China, November 6, 2018.
10. H. Gong, Q. Li, Y. Yu, J.P. Argaud, B. Bouriquet, Y. Maday and O. Mula. A new data-driven approach for reconstruction with noisy data and physical constraints: application to nuclear reactor physics. In Proceedings of ICAPP 2019.
11. Z. Zhang, H. Liao, Q. Li, H. Gong, Z. Chen, X. Li, Q. Liu. Error Analysis of LPD On-line Monitoring System in HPR 1000. Nuclear Power Engineering, 2020,41(2): 11-15. [EI]
12. H. Gong, Z. Chen, Q. Li, S. Cheng. Study on a Data-Enabled Physics-Informed Reactor Physics Operational Digital Twin. Nuclear Power Engineering, 2021, 42(S2):48-53. [EI]
13. H. Gong, Z. Chen, W. Zhao, X. Peng, Q. Li and Y. Yu. Development of a neutron noise simulator with SP3 approximation. Nuclear Science and Engineering. 2021, 41(03)491-499.
14. H. Gong; Q. Li, Q. Liu, X. Li, Z. Lu, J. Wang, Y. Xie, Z. Chen, Y. Yu, X. Peng, K. Liu, R. Guo, B. Zhang and X. Wang. 2D1D Coupled Power Reconstruction Method for On-line Monitoring of PWRs. Atomic Energy Science and Technology, 2021,55(02): 272-278. [EI]
15. Gong, H.; Maday,Y; Mula,O; Taddei,T. PBDW method for state estimation: error analysis for noisy data and nonlinear formulation. arXiv e-prints, p. arXiv:1906. 00810, Jun 2019.[EI]
16. Peng,X*; Li,Q; Zhao,W; Gong, H.; Wang,K. Robust filtering for dynamic compensation of self-powered neutron detectors. Nuclear Engineering and Design,2014, 280: 122-129. [SCI]
17. Yang, Q. H., Yang, Y., Deng, Y. T., He, Q. L., Gong, H. L., & Zhang, S. Q. (2023). Physics-constrained neural network for solving discontinuous interface K-eigenvalue problem with application to reactor physics. Nuclear Science and Techniques, 34(10), 161.
18. Gong, H.; Yu,Y; Li,Q*; Quan, C.Y*. An inverse-distance-based fitting term for 3D-Var data assimilation in nuclear core simulation. Annals of Nuclear Energy, 2020, 141: 107346.[SCI]
19. Gong, H.*; Yu,Y; Li,Q*. Reactor power distribution detection and estimation via a stabilized gappy proper orthogonal decomposition method. Nuclear Engineering and Design, 2020, 370: 110833. [SCI]
20. Gong, H.*; Chen,W; Zhang,C.Y*; Chen,G. Fast solution of neutron diffusion problem with movement of control rods. Annals of Nuclear Energy, 2020, 149:107814. [SCI]
21. Gong, H.*; Chen,Z; Wu,W; Peng, X; Li,Q*. Neutron noise calculation: A comparative study between SP3 theory and diffusion theory. Annals of Nuclear Energy, 2021,156:108184. [SCI]
22. Chen, W.; Di, Y.; Zang, J.; Zhang, C.*; Gong, H.; Xia, B.; Quan, Y.; Wang, L. Study of non-intrusive model order reduction of neutron transport problems. Annals of Nuclear Energy. 2021, 162: 108495. [SCI]
23. Gong, H.*; Chen, Z.; Maday, Y. and Li, Q. Optimal and fast field reconstruction with reduced basis and limited observations: application to reactor core online monitoring. Nuclear Engineering and Design, 2021,377:111113. [SCI]
24. Gong, H.; Cheng, S.; Chen, Z. and Li, Q*. Data-Enabled Physics-Informed Machine Learning for Reduced-Order Modeling Digital Twin: Application to Nuclear Reactor Physics. Nuclear Science and Engineering, Taylor & Francis, 2022, 196, 668-693.[SCI]
25. Gong, H.; Chen, Z. and Li, Q*. Generalized Empirical Interpolation Method with H1 Regularization: Application to Nuclear Reactor Physics. Frontiers in Energy Research. 2022, 9:804018. [SCI]
26. Gong, H.; Cheng, S.; Chen, Z.; Li, Q.*; Quilodrán-Casas, C.; Xiao, D. and Arcucci, R. An efficient digital twin based on machine learning SVD autoencoder and generalised latent assimilation for nuclear reactor physics. Annals of Nuclear Energy. 179(2022):109431.[SCI]
27. Li,W.; Gong, H.; Zang, C*. Solution of Neutron Diffusion Problems by Discontinuous Galerkin Finite Element Method With Consideration of Discontinuity Factors. Journal of Nuclear Engineering and Radiation Science, 2023, 9(3), 031503. [SCI]
28. Yang, Y.; Gong, H.*; Zhang, S.*; Yang, Q.; Chen, Z.; He, Q. and Li, Q. A data-enabled physics-informed neural network with comprehensive numerical study on solving neutron diffusion eigenvalue problems. Annals of Nuclear Energy, 2023, 183, 109656.[SCI]
29. Gong, H.; Zhu, T.*; Chen, Z.; Wan, Y. and Li, Q*. Parameter identification and state estimation for nuclear reactor operation digital twin. Annals of Nuclear Energy,180(2023):109497.[SCI]
30. Yang, Y.; Gong, H.*; He, Q.*; Yang, Q.; Deng, Y.; Zhang, S. On the uncertainty analysis of the data-enabled physics-informed neural network for solving neutron diffusion eigenvalue problem. Nuclear Science and Engineering. 2023. DOI: 10.1080/00295639.2023.2236840.[SCI]
31. Gong Helin, Zhang Shiquan, Yvon Maday. THE OPTIMUM OF EIM MAGIC POINTS AND THE LEAST-SQUARES FORM[J]. Journal on Numerica Methods and Computer Applications, 2023, 44(1): 25-36.
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