Special Session 47: Meeting Point of Scientific Computing and Machine Learning

Numerical analysis for manifold-preserving and data-driven algorithms of high-index saddle dynamics

Xiangcheng Zheng
Shandong University
Peoples Rep of China
Co-Author(s):    Lei Zhang, Pingwen Zhang, Xiangcheng Zheng
Abstract:
High-index saddle dynamics (HiSD) is a powerful instrument in finding multiple saddle points of complex systems. A critical point in designing numerical algorithms is to preserve the manifold properties of HiSD. We perform numerical analysis for manifold-preserving numerical approximation to HiSD, which not only gives error estimates but provides expatiation for manifold-preserving mechanisms of the continuous HiSD. Furthermore, a data-driven HiSD algorithm is presented and analyzed to improve the applicability of the HiSD.