Long-time approximations
of SDEs with convergence rates:
from high dimensional
Langevin dynamics to SPDEs
报告专家：王小捷 教授（中南大学）
报告时间：11月21日（周五）16:00-17:00
报告地点：腾讯会议603-764-137 密码：147258
报告摘要：
Long-time approximations of stochastic differential equations (SDEs) find many applications in the area of statistics，scientific computing and generative artificial intelligence (AI). In this talk, I will present some of our recent progresses on long-time approximations of SDEs with non-contractive coefficients. Both stochastic ordinary differential equations (SODEs) and stochastic partial differential equations (SPDEs) are separately considered in two parts of this talk, where uniform in time error estimates with convergence rates are obtained for the considered discretization schemes.
专家简介：
王小捷，中南大学数学与统计学院教授、博士生导师，本硕博就读于中南大学，2012年获理学博士学位。研究领域为随机微分方程数值方法、数据科学和人工智能中的高维分布采样算法及扩散生成模型、计算金融等。在上述领域取得一系列研究成果，论文发表在SIAM Journal on Numerical Analysis、Mathematics of Computation、SIAM Journal on Scientific Computing、IMA Journal of Numerical Analysis、Journal of Computational Physics、Stochastic Processes and their Applications、Automatica、ICML等计算数学、概率论、自动化领域的主流刊物以及机器学习顶会上。主持国家自然科学基金面上项目多项以及湖南省自然科学基金杰出青年项目等多项科研项目。
邀请人：李彬杰
