Recovering a moving boundary from Cauchy data in an inverse problem which arises in modeling brain tumor treatment: the (quasi)linearization idea combined with radial basis functions (RBFs) approximation

In this paper, the dynamical modeling and behavior analysis of the inverse boundary Stefan problem which promising the understanding of modeling brain tumor treatment, are studied. To umerical simulate these models and to overcome their difficulties such as, non-linearity, free boundary property and having a non-rectangular domain, we propose the use of a strongly meshless technique based on radial basis functions in conjunction with a (quasi)linearization algorithm. Numerical examples are given to show the good accuracy and stability of the presented method.

Automated summary

This paper explores the dynamics modeling and behavior analysis of the inverse boundary Stefan problem, proposing the use of radial basis functions in conjunction with a linearization algorithm to overcome difficulties like non-linearity and free boundary property. Numerical examples demonstrate the accuracy and stability of the method.