| Data-enabled physics-informed machine learning for reduced-order modeling digital twin: application to nuclear reactor physics H Gong, S Cheng, Z Chen, Q Li Nuclear Science and Engineering 196 (6), 668-693, 2022 | 83 | 2022 |
| Sensor placement in nuclear reactors based on the generalized empirical interpolation method JP Argaud, B Bouriquet, F De Caso, H Gong, Y Maday, O Mula Journal of Computational Physics 363, 354-370, 2018 | 76 | 2018 |
| An efficient digital twin based on machine learning SVD autoencoder and generalised latent assimilation for nuclear reactor physics H Gong, S Cheng, Z Chen, Q Li, C Quilodrán-Casas, D Xiao, R Arcucci Annals of nuclear energy 179, 109431, 2022 | 53 | 2022 |
| Stabilization of (G) EIM in presence of measurement noise: application to nuclear reactor physics JP Argaud, B Bouriquet, H Gong, Y Maday, O Mula Spectral and High Order Methods for Partial Differential Equations ICOSAHOM …, 2017 | 48 | 2017 |
| PBDW method for state estimation: error analysis for noisy data and nonlinear formulation H Gong, Y Maday, O Mula, T Taddei arXiv preprint arXiv:1906.00810, 2019 | 35 | 2019 |
| Parameter identification and state estimation for nuclear reactor operation digital twin H Gong, T Zhu, Z Chen, Y Wan, Q Li Annals of Nuclear Energy 180, 109497, 2023 | 30 | 2023 |
| An inverse-distance-based fitting term for 3D-Var data assimilation in nuclear core simulation H Gong, Y Yu, Q Li, C Quan Annals of Nuclear Energy 141 (2020), 107346, 2020 | 30 | 2020 |
| Reactor power distribution detection and estimation via a stabilized gappy proper orthogonal decomposition method H Gong, Y Yu, Q Li Nuclear Engineering and Design 370, 110833, 2020 | 26 | 2020 |
| Optimal and fast field reconstruction with reduced basis and limited observations: Application to reactor core online monitoring H Gong, Z Chen, Y Maday, Q Li Nuclear Engineering and Design 377, 111113, 2021 | 24 | 2021 |
| Relu-kan: New kolmogorov-arnold networks that only need matrix addition, dot multiplication, and relu Q Qiu, T Zhu, H Gong, L Chen, H Ning arXiv preprint arXiv:2406.02075, 2024 | 23 | 2024 |
| A data-enabled physics-informed neural network with comprehensive numerical study on solving neutron diffusion eigenvalue problems Y Yang, H Gong, S Zhang, Q Yang, Z Chen, Q He, Q Li Annals of Nuclear Energy 183, 109656, 2023 | 19 | 2023 |
| The Empirical Interpolation Method applied to the neutron diffusion equations with parameter dependence H Gong, JP Argaud, B Bouriquet, Y Maday Proceedings of PHYSOR, 2016 | 16 | 2016 |
| Physics-constrained neural network for solving discontinuous interface K-eigenvalue problem with application to reactor physics QH Yang, Y Yang, YT Deng, QL He, HL Gong, SQ Zhang Nuclear Science and Techniques 34 (10), 161, 2023 | 15 | 2023 |
| Fast solution of neutron diffusion problem with movement of control rods G Helin, C Wei, Z Chunyu, C Gong Annals of Nuclear Energy 149, 107814, 2020 | 12 | 2020 |
| Data assimilation with reduced basis and noisy measurement: Applications to nuclear reactor cores H Gong Sorbonne Université, 2018 | 12 | 2018 |
| Robust filtering for dynamic compensation of self-powered neutron detectors X Peng, Q Li, W Zhao, H Gong, K Wang Nuclear Engineering and Design 280, 122-129, 2014 | 12 | 2014 |
| On the uncertainty analysis of the data-enabled physics-informed neural network for solving neutron diffusion eigenvalue problem Y Yang, H Gong, Q He, Q Yang, Y Deng, S Zhang Nuclear Science and Engineering 198 (5), 1075-1096, 2024 | 10 | 2024 |
| Generalized Empirical Interpolation Method With H1 Regularization: Application to Nuclear Reactor Physics H Gong, Z Chen, Q Li Frontiers in Energy Research, 897, 2022 | 10 | 2022 |
| A data-driven strategy for xenon dynamical forecasting using dynamic mode decomposition H Gong, Y Yu, X Peng, Q Li Annals of Nuclear Energy 149, 107826, 2020 | 10 | 2020 |
| Reactor field reconstruction from sparse and movable sensors using Voronoi tessellation-assisted convolutional neural networks HL Gong, H Li, D Xiao, S Cheng Nuclear Science and Techniques 35 (5), 43, 2024 | 9 | 2024 |
madayProfesseur de mathématiques appliquées, Université Pierre et Marie Curie, Laboratoire J.-L. Lionsبريد إلكتروني تم التحقق منه على ann.jussieu.fr