| Exploring QCD matter in extreme conditions with Machine Learning K Zhou, L Wang, LG Pang, S Shi Progress in Particle and Nuclear Physics 135, 104084, 2024 | 60 | 2024 |
| Neural network reconstruction of the dense matter equation of state from neutron star observables S Soma, L Wang, S Shi, H Stöcker, K Zhou Journal of Cosmology and Astroparticle Physics 2022 (08), 071, 2022 | 42 | 2022 |
| Reconstructing spectral functions via automatic differentiation L Wang, S Shi, K Zhou Physical Review D 106 (5), L051502, 2022 | 39 | 2022 |
| Tumor radiomics signature for artificial neural network-assisted detection of neck metastasis in patient with tongue cancer YW Zhong, Y Jiang, S Dong, WJ Wu, LX Wang, J Zhang, MW Huang Journal of Neuroradiology 49 (2), 213-218, 2022 | 38 | 2022 |
| Reconstructing the neutron star equation of state from observational data via automatic differentiation S Soma, L Wang, S Shi, H Stöcker, K Zhou Physical Review D 107 (8), 083028, 2023 | 37 | 2023 |
| Deep learning stochastic processes with QCD phase transition L Jiang, L Wang, K Zhou Physical Review D 103 (11), 116023, 2021 | 36 | 2021 |
| Machine learning spatio-temporal epidemiological model to evaluate Germany-county-level COVID-19 risk L Wang, T Xu, T Stoecker, H Stoecker, Y Jiang, K Zhou Machine Learning: Science and Technology 2 (3), 035031, 2021 | 33 | 2021 |
| Rethinking the ill-posedness of the spectral function reconstruction—Why is it fundamentally hard and how Artificial Neural Networks can help S Shi, L Wang, K Zhou Computer Physics Communications 282, 108547, 2023 | 29 | 2023 |
| Local suppression and enhancement of the pairing condensate under rotation L Wang, Y Jiang, L He, P Zhuang Physical Review C 100 (3), 034902, 2019 | 24 | 2019 |
| Continuous-mixture autoregressive networks learning the Kosterlitz-Thouless transition L Wang, Y Jiang, L He, K Zhou Chinese Physics Letters 39 (12), 120502, 2022 | 21* | 2022 |
| Nambu–Jona-Lasinio model in a parallel electromagnetic field L Wang, G Cao, XG Huang, P Zhuang Physics Letters B 780, 273-282, 2018 | 21 | 2018 |
| Diffusion models as stochastic quantization in lattice field theory L Wang, G Aarts, K Zhou Journal of High Energy Physics 2024 (5), 1-30, 2024 | 19 | 2024 |
| Fourier-flow model generating Feynman paths S Chen, O Savchuk, S Zheng, B Chen, H Stoecker, L Wang, K Zhou Physical Review D 107 (5), 056001, 2023 | 16 | 2023 |
| Escape dynamics based on bounded rationality L Wang, Y Jiang Physica A: Statistical Mechanics and its Applications 531, 121777, 2019 | 16 | 2019 |
| Generative Diffusion Models for Lattice Field Theory L Wang, G Aarts, K Zhou arXiv preprint arXiv:2311.03578, 2023 | 15 | 2023 |
| Chiral vortices and pseudoscalar condensation due to rotation L Wang, Y Jiang, L He, P Zhuang Physical Review D 100 (11), 114009, 2019 | 15 | 2019 |
| Competition between magnetic catalysis effect and chiral rotation effect L Wang, G Cao Physical Review D 97 (3), 034014, 2018 | 15 | 2018 |
| Detecting the chiral magnetic effect via deep learning YS Zhao, L Wang, K Zhou, XG Huang Physical Review C 106 (5), L051901, 2022 | 14 | 2022 |
| Mode decomposed chiral magnetic effect and rotating fermions K Fukushima, T Shimazaki, L Wang Physical Review D 102 (1), 014045, 2020 | 12 | 2020 |
| Approaching epidemiological dynamics of COVID-19 with physics-informed neural networks S Han, L Stelz, H Stoecker, L Wang, K Zhou Journal of the Franklin Institute 361 (6), 106671, 2024 | 11 | 2024 |
Kai ZhouThe Chinese University of Hong Kong, ShenzhenVerified email at cuhk.edu.cn
