In this article, the numerical solution of glioma, or glioblastomas, which is one of the most aggressive forms of cancer is considered. A heterogeneous nonlinear diffusion logistic density model is taken as the main focus. To obtain the numerical results, three different discretization techniques: Pseudospectral method (PSM) using Chebyshev–Gauss–Lobatto collocation points, method of lines (MoL), and cubic B-splines (cBS) are employed on the spatial domain, whereas 4th-order Runge–Kutta (RK4) is considered on the time domain. Adapting cBS and PSM discretization to the glioma model is studied at first in this study. In addition to the theoretical convergence results, detailed comparative computational results are presented. All these methods are compared in terms of their efficiencies in varying time step and mesh discretization not only to one another, but also with the methods given in the literature.

在本文中，考虑了胶质瘤或胶质母细胞瘤的数值解决方案，这是最具有侵略性的癌症形式之一。异质非线性扩散Logistic密度模型为主要研究对象。为了获得数值结果，使用了三种不同的离散化技术：在空间域上采用了使用Chebyshev-Gauss-Lobatto配置点的伪光谱方法（PSM），线方法（MoL）和三次B样条（cBS）。在时域上考虑阶次Runge–Kutta（RK4）。本研究首先研究了使cBS和PSM离散化适应神经胶质瘤模型。除理论收敛结果外，还提供了详细的比较计算结果。