报告鸿博体育(集团)有限公司：2019年8月4日（星期日） 15:50-16:50

报告地点：翡翠科教楼B座1710

报告人：陈焱来 教授

工作单位：美国麻省大学达特茅斯分校

报告人简介：

陈焱来博士是美国麻省大学达特茅斯分校数学系终身教授，曾任职博士后于布朗大学应用数学系。他2002年取得中国科学技术大学数学和应用数学学士学位后赴美深造，并于2007年取得美国明尼苏达大学应用数学博士和计算机科学硕士双学位。陈教授曾担任Journal of Scientific Computing特任编辑，并在美国及欧洲著名期刊发表论文近30余篇，他的主要科研方向是大规模高性能科学计算的算法设计和分析。他主持了美国国家科学基金会关于快速算法的两项专项基金，他在此方法上做出的世界独创性的研究被用于数据压缩，机器学习，深度学习，随机方程，分数阶方程等多个新兴领域。

报告简介：

Models of reduced computational complexity and guaranteed accuracy is indispensable in scenarios where a large number of numerical solutions to a sequence of problems are desired in a fast/real-time fashion. Reduced basis method (RBM) is such a paradigm in computational mathematics that can improve efficiency by several orders of magnitudes leveraging a machine learning philosophy, an offline-online procedure, and the recognition that the solution space of the concerned sequence of problems can be well approximated by a smaller space in a tailored fashion. A critical ingredient to guarantee the accuracy of the surrogate solution and guide the construction of the surrogate space is a mathematically rigorous theory. After a brief introduction of RBM, this talk will present one novel and more efficient variant together with some of our recent applications including to solar cell simulation, fast face recognition, and stochastic dynamical systems.