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Chenguang Wan's Resume

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By Chenguang Wan
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Chenguang Wan's Resume
Chenguang Wan Google Scholar ORCID
Birthday: 1995-07-31
Nationality: China
Location: Singapore
Degree: Doctor Degree
E-Mail: chenguang.wan@outlook.com
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EDUCATION

DateInstitutionDegree
Sep. 2017 - Mar. 2023University of Science and Technology of ChinaDoctor - Plasma Physics
Sep. 2013 - Jul. 2017Hefei University of Technology (HFUT)Bachelor - Mechanical Engineering
Sep. 2021 - Oct. 2022École Polytechnique Fédérale de Lausanne (EPFL)Visiting
Jul. 2019 - Aug. 2019National University of Singapore (NUS)Visiting
Sep. 2018 - Oct. 2018National Institute for Fusion Science (NIFS)Visiting

CAREERS

DateInstitutionRole
Feb. 2024 - PresentNanyang Technological University (NTU)Research Fellow
Apr. 2023 - Feb. 2024Hefei Institutes of Physical Science, CASPostdoctoral Associate

SELECTED PUBLICATIONS

  1. M. Wang, C. Wan *, et al., Time series extrinsic regression for reconstructing missing electron temperature in tokamak, Nucl. Fusion, 2025 65 7 076008, doi: 10.1088/1741-4326/addb5f. Citation: 2 IF: 4.0
  2. E. Gao #, C. Wan # *, et al., Bayesian Neural Networks for Predicting Tokamak Energy Confinement Time with Uncertainty Quantification, Nucl. Fusion, 2025 65 08 084001, doi: 10.1088/1741-4326/ade8fd(https://doi.org/10.1088/1741-4326/ade8fd). Citation: 1 IF: 4.0
  3. C. Wan et al., A high-fidelity surrogate model for the ion temperature gradient (ITG) instability using a small expensive simulation dataset, Nucl. Fusion, 2025 65 054001, doi: 10.1088/1741-4326/adc7c9. Citation: 3 IF: 4.0
  4. J. Huang #, C. Wan # * et al.,, Development of real-time density profile inversion with deep neural network model for multi-bands X-mode polarization reflectometer on EAST, Plasma Phys. Control. Fusion, 2025 67, 055002, doi: 10.1088/1361-6587/adc830. Citation: 1 IF: 2.3
  5. C. Wan *, et al. Predict the last closed-flux surface evolution without physical simulation. Nucl. Fusion 2024 64 026014, doi: 10.1088/1741-4326/ad171f. Citation: 9 IF: 4.0
  6. C. Wan *, et al. A machine-learning-based tool for last closed-flux surface reconstruction on tokamaks. 2023 Nuclear Fusion 63, 056019, doi: 10.1088/1741-4326/acbfcc. Citation: 31 IF: 4.0
  7. C. Wan *, et al. EAST discharge prediction without integrating simulation results. 2022 Nucl. Fusion 62 126060, doi: 10.1088/1741-4326/ac9c1a.Citation: 21 IF: 4.0
  8. C. Wan *, et al. A Robust and Fast Data Management System for Machine-Learning Research of Tokamaks. 2022 IEEE Transactions on Plasma Science 50 4980-4986, doi: 10.1109/TPS.2022.3223732. Citation: 5 IF: 1.5
  9. C. Wan, et al. Experiment data-driven modeling of tokamak discharge in EAST. 2021 Nucl. Fusion 61 066015, doi: 10.1088/1741-4326/abf419. Citation: 28 IF: 4.0
  10. X. Deng, C. Wan *, et al. Open-Switch Fault Diagnosis of Three-Phase PWM Converter Systems for Magnet Power Supply on EAST. IEEE Transactions on Power Electronics 2023 38 1064-1078, doi: 10.1109/TPEL.2022.3194113. Citation: 28 IF: 6.5
  11. C. Su, C. Wan *, et al., Interaction AI with Retrieval-enhanced Generation for Knowledge Retrieval in EAST, in 2024 3rd International Conference on Data Analytics, Computing and Artificial Intelligence (ICDACAI), Zakopane, Poland: IEEE, Oct. 2024 965-969, doi: 10.1109/ICDACAI65086.2024.00181. Citation: 0, IF: N/A
  12. B. Cheng, C. Wan *, et al., PCC-Trans: a time series feature selection and model framework for tokamak discharge process in EAST, in International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), Guangzhou, China: SPIE, Jun. 2024 236. doi: 10.1117/12.3034112. Citation: 0, IF: N/A
  13. S. Bai, C. Wan *, et al., Integrated Data-Driven and Physics-Driven Multi-Module Magnetic Equilibrium Calculation and Analysis Tool, in 2024 7th International Conference on Advanced Algorithms and Control Engineering (ICAACE), Shanghai, China: IEEE, Mar. 2024 786-791. doi: 10.1109/ICAACE61206.2024.10549744. Citation: 0, IF: N/A

WORKING PAPERS

  1. C. Wan, et al. Machine learning prediction of plasma behavior from discharge configurations on WEST, submitted to Nucl. Fusion
  2. T. Wang, C. Wan, et al. Plasma-behavior–informed noise compensation and transient disturbance shielding quench detection in superconducting tokamaks, submitted to Nucl. Fusion
  3. Y. Feng #, J. Huang #, C. Wan # *, et al. Fast end-to-end plasma density profile reconstruction from microwave reflectometer data on EAST, submitted to Nucl. Fusion

PROJECTS

DateTitleRole
Jan. 2024 - Dec. 2025Discharge Scenario design based on AIGCHost
Jan. 2021 - Dec. 2025Deep learning ensemble model for tokamak dischargeHost
May. 2020 - Dec. 2025Magnetic field reconstruction and control on EASTPrincipal Executor
Jul. 2018 - May. 2020Tokamak discharge modelingPrincipal Executor

AWARDS

  • Aug. 2023 - Outstanding Doctoral Thesis Nomination of CPS
  • Oct. 2023 - China National Postdoctoral Program for Innovative Talents
  • Dec. 2023 - ITU, AI for Fusion Energy Challenge, Honorable Mention Certificate

SKILLS

  • Good: Database build and management, Tokamak Control, AI for Fusion
  • Modest: C/C++, Web, Docker

Online CV: https://chgwan.github.io/posts/owns/chgwan-cv

This post is licensed under CC BY 4.0 by the author.

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