A Deep Learning Method for Computing Eigenvalues of the Fractional Schr?dinger Operator

作     者：GUO Yixiao MING Pingbing 

作者机构：LSECInstitute of Computational Mathematics and Scientific/Engineering ComputingAcademy of Mathematics and Systems ScienceChinese Academy of Sciences School of Mathematical SciencesUniversity of Chinese Academy of Sciences 

出 版 物：《Journal of Systems Science & Complexity》 (系统科学与复杂性学报(英文版))

年 卷 期：2024年

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 081104[工学-模式识别与智能系统] 08[工学] 070104[理学-应用数学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Natural Science Foundation of China under Grant Nos.12371438 and 12326336 

摘      要：The authors present a novel deep learning method for computing eigenvalues of the fractional Schr?dinger *** proposed approach combines a newly developed loss function with an innovative neural network architecture that incorporates prior knowledge of the *** improvements enable the proposed method to handle both high-dimensional problems and problems posed on irregular bounded *** authors successfully compute up to the first 30 eigenvalues for various fractional Schrodinger *** an application,the authors share a conjecture to the fractional order isospectral problem that has not yet been studied.