ABSTRACT

Recently, solution of discretisation operator technique with circular (DOTWC) approach, and runge-kutta implicit are presented for the Eigen efficient analysis, in this paper based on the various surveys it has been analysed that the efficiency is considered as low and computational burden terms to be high. Henceforth, the implicitly of the restarted Arnoldi (IRA) with intelligent physical system (IPS) with IOT algorithm is implemented for the computational Eigenvalues of critical solution operators in discretized matrices to optimise efficiency and computational burden. Furthermore, we have proposed the Advancement Deep Learning Reinforcement (ADLR) algorithms to improve the efficiency and existing of scalability of the intelligent physical system.

摘要

最近，离散化算子技术与循环（DOTWC）方法的解决方案和runge-kutta隐式被提出用于特征有效分析，在本文中，基于各种调查分析，效率被认为是低和计算负担项要高。此后，对离散矩阵中关键解算子的计算特征值实施了具有智能物理系统（IPS）和 IOT 算法的重新启动的 Arnoldi（IRA）的隐式，以优化效率和计算负担。此外，我们提出了高级深度学习强化（ADLR）算法来提高智能物理系统的效率和可扩展性。