This article provides a brief review of recent developments on two nonlocal operators: fractional Laplacian and fractional time derivative. We start by accounting for several applications of these operators in imaging science, geophysics, harmonic maps and deep (machine) learning. Various notions of solutions to linear fractional elliptic equations are provided and numerical schemes for fractional Laplacian and fractional time derivative are discussed. Special emphasis is given to exterior optimal control problems with a linear elliptic equation as constraints. In addition, optimal control problems with interior control and state constraints are considered. We also provide a discussion on fractional deep neural networks, which is shown to be a minimization problem with fractional in time ordinary differential equation as constraint. The paper concludes with a discussion on several open problems.
翻译:本文简要回顾了两个非本地运营商的最新动态:分拉帕西亚和分时间衍生物。我们首先考虑这些运营商在成像科学、地球物理学、口声图和深(机器)学习方面的若干应用,提出了线性分解方程式的各种解决方案概念,讨论了分拉帕西亚和分时间衍生物的数值办法;特别强调了以线性椭圆方程式作为限制的外部最佳控制问题。此外,还考虑了内控和状态制约的最佳控制问题。我们还讨论了分深神经网络,这证明是一个最小化的问题,一般微分等式的分数是制约因素。文件最后讨论了几个尚未解决的问题。