穆斌 MU BIN  教授（二级）、博导

办公室：同济大学嘉定校区济事楼312 L室

电子邮件：binmu@tongji.edu.cn

主讲课程：

数据库原理与应用（国家双语教学示范课程、上海市重点课程、上海高校示范性全英语教学课程）

研究方向：

人工智能及其可解释性，机器学习，神经网络，大气及海洋大数据分析，智能数据同化

主持或参与科研项目（课题）情况：

1、国家自然科学基金【原创探索计划】项目“物理-数据双驱动的端到端神经同化方法 NeuralDA 研究”，项目批准号42450163，2025-01至2027-12，主持。

2、国家重点研发计划“全球变化及应对”专项项目“大数据与深度学习方法创新地球系统模式发展及应用研究”之课题二“复杂地球系统过程与现象的时空相关性研究”，课题编号2020YFA0608002，2020-11至2025-04，主持。

3、国家自然科学基金联合基金【重点】项目“基于因果推断和物理引导的面向天气预报与气候预测的深度学习理论算法及可解释性研究”，项目编号：U2142211，2022.01-2025.12，第二负责人。

4、国家自然科学基金【面上】项目“多模态数据驱动的海气耦合台风概率预报模型”，项目编号：42075141，2021.01-2024.12，第二负责人。

5、上海市2020年度“科技创新行动计划”社会发展科技攻关“公共安全/突发公共安全事件应急处理处置”专题项目“基于风云卫星智能精准观测针对极端天气事件的长三角航空运行安全应对研究”之课题二“针对CNOP的高效智能算法开发与应用“，课题编号 20dz1200702， 2020-09-01至2023-08-31，第二负责人。

6、2019年重点领域学科交叉重大“中央高校基本科研业务费专项资金”项目“基于深度神经网络的台风路径强度和降水精准预报研究”，项目编号22120190207，2019-08-01日至2021-07-31，主持。

7、国家重点基础研究发展计划（973计划）课题“信息服务的运行支撑平台及在交通和医疗信息服务中的实证研究”，课题编号 2010CB328106，第二负责人。

8、江苏省信息化建设重点示范工程项目“太仓市智慧城市大数据智能分析系统研发”，第二负责人。

学术论文：

【1】TianXing: A Linear Complexity Transformer Model with Explicit Attention Decay for Global Weather Forecasting ，Yuan, Shijin; Wang, Guansong; Mu, Bin; Zhou, Feifan，Advances in Atmospheric Sciences | 2025年

【2】Incorporating heat budget dynamics in a Transformer-based deep learning model for skillful ENSO prediction，Mu, Bin; 崔悦涵; Yuan, Shijin; Qin, Bo，NPJ CLIMATE AND ATMOSPHERIC SCIENCE | 2024年 | 7卷 | 1期

【3】基于深度学习的全球热带气旋生成预测模型及其可解释性分析，穆斌; 王馨; 袁时金; 陈宇轩; 王冠淞等7名作者，中国科学:地球科学 | 2024年 | 54卷 | 12期 | 3708-3733页

【4】Multivariate Upstream Kuroshio Transport (UKT) Prediction and Targeted Observation Sensitive Area Identification of UKT Seasonal Reduction，穆斌; Yang-Hu, Yifan; Qin, Bo; 袁时金，OCEAN MODELLING | 2024年 | 189卷

【5】A generative adversarial network-based unified model integrating bias correction and downscaling for global SST，袁时金; 冯新; 穆斌; Qin, Bo; Wang, Xin等6名作者，Atmospheric and Oceanic Science Letters | 2024年 | 17卷 | 1期

【6】Toward a Learnable Climate Model in the Artificial Intelligence Era，Huang, Gang; Wang, Ya; Ham, Yoo-Geun; 穆斌; Tao, Weichen等6名作者，Advances in Atmospheric Sciences | 2024年 | 41卷 | 7期 | 1281-1288页

【7】A deep learning-based bias correction model for Arctic sea ice concentration towards MITgcm，袁时金; 朱师辰; Luo, Xiaodan; 穆斌，Ocean Modelling | 2024年 | 188卷

【8】Developing intelligent Earth System Models: An AI framework for replacing sub-modules based on incremental learning and its application，穆斌; 赵紫君; 袁时金; Qin, Bo; Dai, Guo-Kun等6名作者，Atmospheric Research | 2024年 | 302卷

【9】An extension to ensemble forecast of conditional nonlinear optimal perturbation considering nonlinear interaction between initial and model parametric uncertainties ，Mu, Bin; Zhao, Zi-Jun; Yuan, Shi-Jin; Chen, Xing-Rong; Qin, Bo等6名作者，Atmospheric Research | 2024年 | 311卷

【10】A deep learning-based global tropical cyclogenesis prediction model and its interpretability analysis，Mu, Bin; Wang, Xin; Yuan, Shijin; Chen, Yuxuan; 王冠淞等7名作者，Science China Earth Sciences | 2024年

【11】IceTFT v1.0.0: interpretable long-term prediction of Arctic sea ice extent with deep learning，穆斌; 罗晓丹; 袁时金; Liang, Xi，GEOSCIENTIFIC MODEL DEVELOPMENT | 2023年 | 16卷 | 16期 | 4677-4697页

【12】A paralleled embedding high-dimensional Bayesian optimization with additive Gaussian kernels for solving CNOP，袁时金; 刘娅璇; Qin, Bo; 穆斌; Zhang, Kun，Ocean Modelling | 2023年 | 184卷

【13】A radiative transfer deep learning model coupled into WRF with a generic fortran torch adaptor，穆斌; 陈璐; 袁时金; Qin, Bo，FRONTIERS IN EARTH SCIENCE | 2023年 | 11卷

【14】Dimension shifting based intelligent algorithm framework to solve conditional nonlinear optimal perturbation，袁时金; 刘娅璇; Zhang, Huazhen; 穆斌，Computers and Geosciences | 2023年 | 176卷

【15】NAO Seasonal Forecast Using a Multivariate Air–Sea Coupled Deep Learning Model Combined with Causal Discovery，穆斌; 姜欣; 袁时金; 崔悦涵; Qin, Bo，Atmosphere | 2023年 | 14卷 | 5期

【16】Error Evolutions and Analyses on Joint Effects of SST and SL via Intermediate Coupled Models and Conditional Nonlinear Optimal Perturbation Method，穆斌; 秦小云; 袁时金; Qin, Bo，JOURNAL OF MARINE SCIENCE AND ENGINEERING | 2023年 | 11卷 | 5期

【17】Estimating the tropical cyclone wind structure using physics-incorporated networks，袁时金; 尤钱湖; 穆斌; 秦博; Xu Jing，FRONTIERS IN EARTH SCIENCE | 2023年 | 10卷

【18】PIRT: A Physics-Informed Red Tide Deep Learning Forecast Model Considering Causal-Inferred Predictors Selection，穆斌; 秦博; 袁时金; Wang, Xin; Chen, Yuxuan，IEEE Geoscience and Remote Sensing Letters | 2023年 | 20卷

【19】CAU: A Causality Attention Unit for Spatial-temporal Sequence Forecast，Qin, Bo; Meng, Fanqing; Fang, Xianghui; Dai, Guokun; 袁时金等6名作者，IEEE Transactions on Multimedia | 2023年 | 1-15页

【20】ENSO-GTC: ENSO Deep Learning Forecast Model With a Global Spatial-Temporal Teleconnection Coupler，穆斌; 秦博; 袁时金，Journal of Advances in Modeling Earth Systems | 2022年 | 14卷 | 12期

【21】Feature extraction-based intelligent algorithm framework with neural network for solving conditional nonlinear optimal perturbation，袁时金; 张华桢; 刘娅璇; 穆斌，Soft Computing | 2022年 | 26卷 | 14期 | 6907-6924页

【22】A deep learning urban traffic congestion forecast model blending the temporal continuity and periodicity，穆斌; Huang, Yuxi，ACM International Conference Proceeding Series | 2022年 | 602-607页

【23】Ensemble Forecast for Tropical Cyclone Based on CNOP-P Method: A Case Study of WRF Model and Two Typhoons，袁时金; Shi Bo; 赵紫君; 穆斌; Zhou Fei-fan等6名作者，JOURNAL OF TROPICAL METEOROLOGY | 2022年 | 28卷 | 2期 | 121-138页

【24】Simulation, precursor analysis and targeted observation sensitive area identification for two types of ENSO using ENSO-MC v1.0 ，穆斌; 崔悦涵; 袁时金; 秦博，GEOSCIENTIFIC MODEL DEVELOPMENT | 2022年 | 15卷 | 10期 | 4105-4127页

【25】Optimal Precursors Identification for North Atlantic Oscillation Using the Parallel Intelligence Algorithm，穆斌; 李婧; 袁时金; 罗晓丹; Dai, Guokun，Scientific Programming | 2022年 | 2022卷

【26】The NAO Variability Prediction and Forecasting with Multiple Time Scales Driven by ENSO Using Machine Learning Approaches，穆斌; 李婧; 袁时金; Luo, Xiaodan，Computational Intelligence and Neuroscience | 2022年 | 2022卷

【27】GCN Model combined with Bi-GRU for traffic prediction，穆斌; Zhen, Lin，Proceedings of SPIE - The International Society for Optical Engineering | 2022年 | 12259

【28】ENSO-ASC 1.0.0: ENSO deep learning forecast model with a multivariate air-sea coupler，穆斌; 秦博; 袁时金，GEOSCIENTIFIC MODEL DEVELOPMENT | 2021年 | 14卷 | 11期 | 6977-6999页

【29】The ELM Model with Residual Compensation Based on ARIMA for North Atlantic Oscillation Index Prediction，Luo, Xiaodan; 袁时金; 穆斌; Li, Jing，ACM International Conference Proceeding Series | 2021年 | 122-126页

【30】An improved continuous tabu search algorithm with adaptive neighborhood radius and increasing search iteration times strategies，袁时金; 徐运佳; 穆斌; Zhang, Linlin; Ren, Juhui等7名作者，International Journal on Artificial Intelligence Tools | 2021年 | 30卷 | 2期

【31】Typhoon Intensity Forecasting Based on LSTM Using the Rolling Forecast Method，袁时金; Wang, Cheng; 穆斌; Zhou, Feifan; Duan, Wansuo，ALGORITHMS | 2021年 | 14卷 | 3期

【32】Efficient executions of community earth system model onto accelerators using GPUs，袁时金; Wang, Cheng; 穆斌; 罗晓丹，ACM International Conference Proceeding Series | 2020年 | 192-199页

【33】CNOP-P-Based Parameter Sensitivity Analysis for North Atlantic Oscillation in Community Earth System Model Using Intelligence Algorithms，穆斌; 李婧; 袁时金; 罗晓丹; Dai, Guokun，ADVANCES IN METEOROLOGY | 2020年 | 2020卷

【34】Applying Convolutional LSTM Network to Predict El Niño Events: Transfer Learning from the Data of Dynamical Model and Observation，穆斌; Ma, Shaoyang; 袁时金; Xu, Hui，ICEIEC 2020 - Proceedings of 2020 IEEE 10th International Conference on Electronics Information and Emergency Communication，2020年 | 215-219页

【35】Data Assimilation by Artificial Neural Network using Conventional Observation for WRF Model，袁时金; Shi, Bo; 穆斌，ACM International Conference Proceeding Series | 2020年 | 62-67页

【36】Multi-scale downscaling with bayesian convolution network for ENSO SST pattern，穆斌; 秦博; 袁时金，Proceedings - 2020 5th International Conference on Electromechanical Control Technology and Transportation, ICECTT 2020 | 2020年 | 359-362页

【37】A Climate Downscaling Deep Learning Model considering the Multiscale Spatial Correlations and Chaos of Meteorological Events，穆斌; 秦博; 袁时金; 秦小云，Mathematical Problems in Engineering | 2020年 | 2020卷

【38】Prediction of north atlantic oscillation index associated with the sea level pressure using DWT-LSTM and DWT-ConvLSTM networks，穆斌; 李婧; 袁时金; 罗晓丹，Mathematical Problems in Engineering | 2020年 | 2020卷

【39】Applying Convolutional LSTM Network to Predict El Nino Events: Transfer Learning from The Data of Dynamical Model and Observation，穆斌; 马少阳; 袁时金; Xu, Hui，PROCEEDINGS OF 2020 IEEE 10TH INTERNATIONAL CONFERENCE ON ELECTRONICS INFORMATION AND EMERGENCY COMMUNICATION (ICEIEC 2020) | 2020年 | 215-219页

【40】NAO Index Prediction using LSTM and ConvLSTM Networks Coupled with Discrete Wavelet Transform，穆斌; 李婧; 袁时金; 罗晓丹; Dai, Guokun，Proceedings of the International Joint Conference on Neural Networks | 2019年 | 2019-July卷，匈牙利布达佩斯

【41】ENSO Forecasting over Multiple Time Horizons Using ConvLSTM Network and Rolling Mechanism，穆斌; Peng, Cheng; 袁时金; Chen, Lei，Proceedings of the International Joint Conference on Neural Networks | 2019年 | 2019-July卷，匈牙利布达佩斯

【42】Identifying Typhoon Targeted Observations Sensitive Areas Using the Gradient Definition Based Method，穆斌; Ren, Juhui; 袁时金; Zhou, Feifan，ASIA-PACIFIC JOURNAL OF ATMOSPHERIC SCIENCES | 2019年 | 55卷 | 2期 | 195-207页

【43】Prediction of north atlantic oscillation index with convolutional LSTM based on ensemble empirical mode decomposition，袁时金; 罗晓丹; 穆斌; Li, Jing; Dai, Guokun，Atmosphere | 2019年 | 10卷 | 5期

【44】INTELLIGENT ALGORITHMS FOR SOLVING CNOP AND THEIR APPLICATIONS IN ENSO PREDICTABILITY AND TROPICAL CYCLONE ADAPTIVE OBSERVATIONS，穆斌; Zhang Lin-lin; 袁时金; 钱一闻; 温仕成等7名作者，JOURNAL OF TROPICAL METEOROLOGY | 2019年 | 25卷 | 1期 | 63-81页

【45】The Optimal Precursors for ENSO Events Depicted Using the Gradientdefinition-based Method in an Intermediate Coupled Model ，穆斌; Ren, Juhui; 袁时金; Zhang, Rong-Hua; Chen, Lei等6名作者，Advances in Atmospheric Sciences | 2019年 | 36卷 | 12期 | 1381-1392页

【46】Optimal precursors of double-gyre regime transitions with an adjoint-free method，袁时金; 李糜; Wang, Qiang; Zhang, Kun; 张华桢等6名作者，Journal of Oceanology and Limnology | 2019年 | 37卷 | 4期 | 1137-1153页

【47】CNOP-P-based parameter sensitivity for double-gyre variation in ROMS with simulated annealing algorithm，袁时金; 张华桢; 李糜; 穆斌，Journal of Oceanology and Limnology | 2019年 | 37卷 | 3期 | 957-967页

【48】A modified direct search algorithm based on kernel density estimator with three mapping strategies for solving nonlinear optimization，Zhang, Lin-Lin; 穆斌; 袁时金，Journal of Computers (Taiwan) | 2019年 | 30卷 | 4期 | 17-30页

【49】Parallel PCA-Based Bacterial Foraging Optimization Algorithm for Identifying Optimal Precursors of North Atlantic Oscillation，穆斌; Jing Li; 袁时金; 罗晓丹; Guokun Dai，2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS). Proceedings | 2019年 | 1171-7页

【50】A novel approach for solving CNOPs and its application in identifying sensitive regions of tropical cyclone adaptive observations，Zhang, Linlin; 穆斌; 袁时金; Zhou, Feifan，NONLINEAR PROCESSES IN GEOPHYSICS | 2018年 | 25卷 | 3期 | 693-712页

【51】Parallel dynamic search fireworks algorithm with linearly decreased dimension number strategy for solving conditional nonlinear optimal perturbation，穆斌; 赵珺晖; 袁时金; 颜景豪，Proceedings of the International Joint Conference on Neural Networks | 2017年 | 2017-May卷 | 2314-2321页，美国阿拉斯加

【52】CNOP-Based Sensitive Areas Identification for Tropical Cyclone Adaptive Observations with PCAGA Method ，Zhang, Lin-Lin; 袁时金; 穆斌; Zhou, Fei-Fan，ASIA-PACIFIC JOURNAL OF ATMOSPHERIC SCIENCES | 2017年 | 53卷 | 1期 | 63-73页

【53】An efficient approach based on the gradient definition for solving conditional nonlinear optimal perturbation ，穆斌; Ren, Juhui; 袁时金，Mathematical Problems in Engineering | 2017年 | 2017卷

【54】CACO-LD: Parallel Continuous Ant Colony Optimization with Linear Decrease Strategy for Solving CNOP，袁时金; 陈韵怡; 穆斌，Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | 2017年 | 10637 LNCS卷 | 494-503页

【55】Parallel Modified Artificial Bee Colony Algorithm for Solving Conditional Nonlinear Optimal Perturbation，Ren, Juhui; 袁时金; 穆斌，Proceedings - 18th IEEE International Conference on High Performance Computing and Communications, 14th IEEE International Conference on Smart City and 2nd IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2016 | 2016年 | 333-340页，澳大利亚悉尼

【56】PCAFP for Solving CNOP in Double-Gyre Variation and Its Parallelization on Clusters，袁时金; 李糜; 穆斌; Wang, Jingpeng，Proceedings - 18th IEEE International Conference on High Performance Computing and Communications, 14th IEEE International Conference on Smart City and 2nd IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2016 | 2016年 | 284-291页，澳大利亚悉尼

【57】PCGD: Principal components-based great deluge method for solving CNOP，温仕成; 袁时金; 穆斌; Li, Hongyu; Ren, Juhui，2015 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC) | 2015年 | 1513-1520页

【58】PCAGA: Principal Component Analysis Based Genetic Algorithm for Solving Conditional Nonlinear Optimal Perturbation，穆斌; Zhang, Linlin; 袁时金; Li, Hongyu，2015 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | 2015年

【59】Parallel dynamic step size sphere-gap transferring algorithm for solving conditional nonlinear optimal perturbation，袁时金; 颜景豪; 穆斌; Li, Hongyu，Proceedings - 2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security and 2015 IEEE 12th International Conference on Embedded Software and Systems, HPCC-CSS-ICESS 2015 | 2015年 | 559-565页

【60】PPSO: PCA based particle swarm optimization for solving conditional nonlinear optimal perturbation，穆斌; 温仕成; 袁时金; Li, Hongyu，Computers and Geosciences | 2015年 | 83卷 | 65-71页

【61】A Parallel Sensitive Area Selection-Based Particle Swarm Optimization Algorithm for Fast Solving CNOP，Yuan Shijin, Ji Feng, Yan Jinghao, Mu Bin，22nd International Conference on Neural Information Processing (ICONIP)，土耳其伊斯坦布尔

【62】Parallel Cooperative Co-evolution Based Particle Swarm Optimization Algorithm for Solving Conditional Nonlinear Optimal Perturbation，Yuan Shijin, Zhao Li, Mu Bin，22nd International Conference on Neural Information Processing (ICONIP)，土耳其伊斯坦布尔

【63】Parallel dynamic step size sphere-gap transferring algorithm for solving conditional nonlinear optimal perturbation，Yuan Shijin, Yan Jinghao, Mu Bin, Li Hongyu，17th IEEE International Conference on High Performance Computing and Communications, IEEE 7th International Symposium on Cyberspace Safety and Security and IEEE 12th International Conference on Embedded Software and Systems, 美国纽约

【64】PCAGA: Principal Component Analysis Based Genetic Algorithm for Solving Conditional Nonlinear Optimal Perturbation，Bin Mu，Linlin Zhang，Shijin Yuan，Hongyu Li，2015 International Joint Conference on Neural Networks (IJCNN)，爱尔兰基拉尼

【65】User-QoS-based Web Service Clustering for QoS Prediction，Fuxin Chen, Shijin Yuan, Bin Mu，the 22nd IEEE International Conference on Web Services， CCF-B，美国纽约

【66】PCGD: Principal components-based great deluge method for solving CNOP，Wen, Shicheng，Yuan, Shijin，Mu, Bin，Li, Hongyu，Ren, Juhui，IEEE Congress on Evolutionary Computation, CEC 2015，日本仙台

其他

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