数学与系统科学研究院
计算数学所学术报告:
报告人： Wen CHEN
Scientific Computing Department
Simula Research Laboratory Oslo, Norway
报告题目: Kernel distance (radial
basis) function and wavelets for multiscale multivariate scattered data processing and meshfree numerical
PDE
Abstract: The first part of this talk is a brief survey of the representative
meshfree methods, major applications, promises and problems, and main players
in this very active arena. Then, the second part of this talk will focus on the
distance (radial basis) function and their applications to numerical PDE. It is
noted that the distance functions can handle arbitrarily high-dimensional scattered
data in a very easy and natural fashion.
It is well know
that the standard wavelets has little to do with the solution of PDE and ceases
to work well for multidimensional scattered data problems, while the common
distance functions have no multiresolution capacity. Based on the fact that the
distance function underlies the kernel solution of partial differential
equations, we recently developed the kernel distance function wavelets, which
combines the strengths of the wavelets and the distance function to handle
multiscale multivariate scattered data problems. We also introduced a few new
distance function numerical discretization techniques which are truly meshfree,
integration-free, spectral convergent, symmetric, and easy-to-implement.
报告时间：2003年4月2日 上午10:00
报告地点：科技综合楼三层报告厅