常海濱，男，遼甯清原人，1983年12月生。本科畢業于吉林大學數學學院，博士畢業于北京大學工學院，之後在北京大學從事了博士後和研究崗位工作，于2022年6月入職bevictor伟德官网bevictor伟德官网。主要研究方向為滲流數值模拟、開發優化、數據同化和機器學習。代表性成果：（1）提出了複雜儲層介質（如岩相、裂縫、渠道）的反演算法；（2）提出了用于強非線性問題（如流固耦合問題）的疊代數據同化算法；（3）提出了數據驅動的物理規律挖掘方法；（4）提出了融合領域知識的深度學習方法。在Journal of Computational Physics、Computer Methods in Applied Mechanics and Engineering、Journal of Geophysical Research: Solid Earth、SPE Journal和Journal of Hydrology等國際知名雜志發表SCI論文20餘篇。擔任10餘國際知名期刊審稿人。獲授權國家發明專利1項。主持/參與博士後基金、國家自然科學基金、國家科技重大專項子課題和橫向課題等項目10餘項。
Short Biosketch of Dr. Haibin Chang
Haibin Chang was born in 1983, Liaoning, China. He received bachelor’s degree in mechanics from Jilin University, China, and received PhD degree in energy and resources engineering from Peking University, China. After that he sequentially worked as post doctor and research scientist in Peking University. He joined China University of Mining and Technology (Beijing) in June 2022. His research interests include subsurface flow simulation, production optimization, data assimilation, and machine learning.Hepublishedover 20journal papers, which are published in high quality journals, such as《Journal of Computational Physics》,《Computer Methods in Applied Mechanics and Engineering》,《Journal of Geophysical Research: Solid Earth》,《SPE Journal》and《Journal of Hydrology》.His 10 representative papers are listed below.
代表性論文：
[1] Wang, N., Chang, H. *, Zhang, D. *, Efficient uncertainty quantification for dynamic subsurface flow with surrogate by Theory-guided Neural Network. Computer Methods in Applied Mechanics and Engineering. 2021, 373, 113492.
[2] Wang, N., Chang, H. *, Zhang, D. *, Deep-Learning-Based Inverse Modeling Approaches: A Subsurface Flow Example. Journal of Geophysical Research: Solid Earth. 2021, 126(2), e2020JB020549.
[3]Xu, H., Chang, H. *, Zhang, D.*, DLGA-PDE: Discovery of PDEs with incomplete candidate library via combination of deep learning and genetic algorithm. Journal of Computational Physics. 2020, 584, 124700.
[4]Chang, H., Zhang, D., Machine learning subsurface flow equations from data. Computational Geosciences. 2019, 23(5), 895-910.
[5]Chang, H., Zhang, D., Identification of physical processes via combined data-driven and data-assimilation methods. Journal of Computational Physics. 2019, 393, 337-350.
[6]Chang, H., Zhang, D., History matching of stimulated reservoir volume of shale gas reservoirs using an iterative ensemble smoother. SPE Journal. 2018, 23(2), 346 - 366.
[7]Chang, H., Liao, Q., Zhang, D., Surrogate model based iterative ensemble smoother for subsurface flow data assimilation. Advances in Water Resources. 2017, 100, 96-108.
[8]Chang, H., Liao, Q., Zhang, D., Benchmark problems for subsurface flow uncertainty quantification. Journal of Hydrology. 2015, 531, 168-186.
[9]Chang, H., Zhang, D., Lu, Z., History matching of facies distribution with the EnKF and level set parameterization. Journal of Computational Physics. 2010, 229, 8011-8030.
[10]Chang, H., Chen, Y., Zhang, D., Data Assimilation of Coupled Fluid Flow and Geomechanics Using the Ensemble Kalman Filter. SPE Journal. 2010, 15(2), 382-394.
Contact: Haibin Chang
School of Energy and Mining Engineering
China University of Mining and Technology (Beijing)
Ding 11 Xueyuan Road, Beijing 100083, P. R. China
Email: hb-chang@163.com
