袁春鑫博士，本科毕业于中国海洋大学海洋科学专业，博士毕业于University College London数学系，目前就职于中国海洋大学数学科学学院、海洋数学技术联合实验室。主要从事非线性波动、海洋内波、海洋涡旋与环流的研究，侧重于数学理论与物理海洋学的交叉，其研究成果以第一作者身份发表在Journal of Fluid Mechanics、Journal of Physical Oceanography等国际权威期刊上。
Over the last few decades,internal solitary waves in the ocean have garnered increased attention, owing to advances in understanding its great impact on oceanic energy cascade, climate system, marine engineering, underwater navigation and ocean ecological systems. Nevertheless, the primary control equation- the Navier-Stokes equation- is difficult to conduct analysis and to obtain the insight into their dynamics. Thus, we usually derive the reduced model based on physical laws and asymptotic method. In this presentation, we would like to present some work on the derivation and application of reduced model for internal solitary waves. AI method get more involved in the research of internal solitary waves and the fact is that pure data-driven Neural Network is usually difficult to obtain satisfactory results, the reason can be attributed to the lack of sufficient training data due to the difficultness of getting observational data of vertical structure and to the complexness of evolution of internal solitary waves which are in three spatial dimensions and one temporal dimension. Thus, one way to promote the performance of Neural Network is to get the aforementioned reduced models involved.
