| 报告题目： | Reduced basis methods and their application in data science |
| 报 告 人： | Prof. Chen Yanlai |
| University of Massachusetts Dartmouth | |
| 报告时间： | 7月15日（周三）早上10：00-11：00 |
| 报告地点： | 九龙湖数学系第一报告厅 |
| 相关介绍： | 摘要：Models of reduced computational complexity is indispensable in scenarios where a large number of numerical solutions to a parametrized problem are desired in a fast/real-time fashion. These include simulation-based design, parameter optimization, optimal control, multi-model/scale analysis, uncertainty quantification. Thanks to an offline-online procedure and the recognition that the parameter-induced solution manifolds can be well approximated by finite-dimensional spaces, reduced basis method (RBM) and reduced collocation method (RCM) can improve efficiency by several orders of magnitudes. The accuracy of the RBM solution is maintained through a rigorous a posteriori error estimator whose efficient development is critical. In this talk, I will give a brief introduction of the RBM, discuss recent and ongoing efforts to develop RCM, and explain how the newly-designed Reduced Basis Decomposition can be used for data compression and face recognition. |
