Number of Solitons Emerged in the Initial Profile of Shallow Water Using Convolutional Neural Networks

The soliton resolution conjecture proposes that the initial value problem can evolve into a dispersion part and a soliton part.However,the problem of determining the number of solitons that form in a given initial profile remains unsolved,except for a few specific cases.In this paper,the authors use the deep learning method to predict the number of solitons in a given initial value of the Korteweg-de Vries(KdV)equation.By leveraging the analytical relationship between Asech^(2)(x)initial values and the number of solitons,the authors train a Convolutional Neural Network(CNN)that can accurately identify the soliton count from spatio-temporal data.The trained neural network is capable of predicting the number of solitons with other given initial values without any additional assistance.Through extensive calculations,the authors demonstrate the effectiveness and high performance of the proposed method.

“食品+X”多学科交叉融合背景下食品学科研究生课程体系建设与探索

研究生教育是我国拔尖创新人才培养工作的主体,但其教育模式依旧属于典型的科研应试型传统模式,学生在知识融合、科技创新、成果转化等方面存在明显不足。食品学科与理、工、农、医、文等多学科交叉融合,不仅可以提升传统学科的建设层次和发展水平,也能催生新的学科领域及创新型人才。该文重点介绍了“食品+X”研究生课程体系建设现阶段存在的问题,并总结了相应的课程体系建设举措,以期增强我国食品学科研究生在世界竞争性发展中的知识储备能力与创新能力。