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第 5 期            倪 菲,等:物理信息机器学习驱动的高速磁浮列车悬浮系统动力学响应预测                                        1377

                  in  Applied  Mechanics  and  Engineering, 2024, 420:  metrics, and  comparisons[J].  Journal  of  Computational
                  116721.                                           Physics,2023,477:111902.
              [25]  YANG Y B,PERDIKARIS P. Adversarial uncertainty quan-  [32]  SUN H K,XU C Y,WU G X,et al. A two-stage physical-
                  tification  in  physics-informed  neural  networks[J].  Journal  of  informed  neural  network  approach  for  high-speed  railway
                  Computational Physics,2019,394:136-152.           track  geometry  irregularity  maintenance[J].  IEEE  Transac-
              [26]  LIU  X, YAO  W, PENG  W, et  al.  Bayesian  physics-  tions on Intelligent Transportation Systems,2025,26(8):
                  informed  extreme  learning  machine  for  forward  and  inverse  12080-12092.
                  PDE  problems  with  noisy  data[J].  Neurocomputing, 2023,
                                                                [33]  LIU Y F,YANG L,ZHANG S F,et al. Parameter identifi-
                  549:126425.
                                                                    cation and platoon control for virtually coupled train set with
              [27]  DABROWSKI J J,PAGENDAM D E,HILTON J,et al.
                                                                    physics-informed neural network dynamics model[J]. Vehicle
                  Bayesian physics informed neural networks for data assimila-
                                                                    System Dynamics,2025,63(1):71-92.
                  tion  and  spatio-temporal  modelling  of  wildfires[J].  Spatial
                                                                [34]  SUN  H  K, GAO  Y, XU  C  Y, et  al.  High-speed  railway
                  Statistics,2023,55:100746.
                                                                    track geometry robust maintenance with Bayesian deep learn-
              [28]  PEREZ S,MADDU S,SBALZARINI I F,et al. Adaptive
                                                                    ing-enhanced   physical-informed   neural   networks[J].
                  weighting  of  Bayesian  physics  informed  neural  networks  for
                                                                    Computer-Aided  Civil  and  Infrastructure  Engineering,
                  multitask  and  multiscale  forward  and  inverse  problems[J].
                                                                    2025,40(24):3934-3952.
                  Journal of Computational Physics,2023,491:112342.
                                                                [35]  靖永志. 高速磁浮列车悬浮间隙传感器及其建模方法研究
              [29]  ZOU Z R,MENG X H,KARNIADAKIS G E. Correcting
                                                                    [D]. 成都:西南交通大学,2014.
                  model  misspecification  in  physics-informed  neural  networks
                  (PINNs)[J]. Journal of Computational Physics,2024,505:  JING Yongzhi. Study on the gap sensor of high-speed maglev
                                                                    train and its modeling method[D]. Chengdu:Southwest Jiao-
                  112918.
              [30]  LIU H L,WANG Z,DENG R,et al. Flow reconstruction  tong University,2014.
                  with  uncertainty  quantification  from  noisy  measurements
                  based  on  Bayesian  physics-informed  neural  networks[J].  第一作者:倪 菲(1985—),女,博士,副教授。
                  Physics of Fluids,2024,36(11):117104.                 E-mail:fei.ni@tongji.edu.cn
              [31]  PSAROS A F,MENG X H,ZOU Z R,et al. Uncertainty  通信作者:范 琳(1999—),女,博士研究生。
                  quantification  in  scientific  machine  learning: methods,  E-mail:fanlin@tongji.edu.cn
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