2024年1月5日上午，孟加拉南北大学M.Monir Uddin副教授应邀来我院致远楼543会议室做题为“Computational Techniques for Model Order Reduction of Large-Scale Sparse Descriptor Systems”的学术报告会。报告由自动化系主任关燕鹏老师主持，我院及计算机科学与技术学院相关专业师生参加了此次报告会。

计算模拟是近年来的一个热点研究领域。为了模拟，引入一种实体模式并进行数学表示，通常情况下这些实际场景中的数学表示是线性时不变连续时间系统。这些系统往往具有额外的代数约束条件，从而形成微分代数方程或描述符系统。由于计算机内存限制，有时生成的系统太大而无法存储。基于此，M.Monir Uddin副教授向我们深入介绍了将高维系统转换为显著简化的系统的模型降阶（MOR）技术。报告中，M.Monir Uddin教授首先给出了基于系统辨识的降阶数据驱动物理模型，接着利用进化算法提出模型降阶技术，并展示了将基于数据驱动的模型降阶技术应用于交通网络、人工智能神经网络等实际场景的情况。

会后，M.Monir Uddin教授与参会师生就模型降低相关问题、研究生联合培养等方面的问题进行了深入的交流与探讨，报告会在热烈地掌声中圆满结束！

专家简介：

M. Monir Uddin is an Associate Professor in the Department of Mathematics and Physics at North South University, Bangladesh. He received PhD in Mathematics from the Max Planck Institute (MPI) for Dynamics of Complex Technical Systems, Germany in 2015. During his PhD Research (2011-2015), he was a member of the International Max Planck Research School for Advanced Methods in Process and Systems Engineering Magdeburg, Germany (2012-2015). He completed his second M.Sc. in Applied Mathematics from Stockholm University, Sweden in 2011. He completed B.Sc. (2003) and M.Sc. (2005) in Mathematics from the University of Chittagong, Bangladesh. Additionally, He worked as a researcher in the Department of Mathematics at Stockholm University and also in the Department of Mathematics in Industry and Technology at Chemnitz University of Technology, Germany. He has teaching experiences in many universities across the world. He published around 60 articles in internationally reputed Journal and conferences. He is the author of the books Computational Methods for Approximation of Large-Scale Dynamical Systems.  His research interests include model order reduction of large-scale dynamical systems, scientific computing, data driven modeling and Machine learning for dynamical process.

撰稿人：关燕鹏

初   审：关燕鹏

二   审：高   慧

三   审：郝   践