报告人：Dong Wang（University of Utah）
时 间：2019年1月3日 上午 9:00—10:00
地 点：学术报告厅111
题目：Diffusion generated methods for target-valued maps
摘要：
A variety of tasks in inverse problems and data analysis can be formulated as the variational problem of minimizing the Dirichlet energy of a function that takes values in a certain target set and possibly satisfies additional constraints. These additional constraints may be used to enforce fidelity to data or other structural constraints arising in the particular problem considered. We will present diffusion generated methods for solving this problem for a wide class of target sets and prove stability and convergence. We will give examples of how these methods can be used for the geometry processing task of finding Dirichlet partitions, constructing smooth orthogonal matrix valued functions, and solving inverse problems for target valued maps. This is joint work with Braxton Osting.