Short Courses (Aug 11--12, 2019)

Lecture 1. An Introduction to GMG and AMG

Speaker: Ludmil Zikatanov, Penn State University

Multigrid methods (MGs) is a name used for a suite of advanced techniques for the solution of linear systems. We will introduce the basic components of geometric multigrid methods (GMGs) and algebraic multigrid methods (AMGs) such as smoothing via iterative methods, coarsening using adjacency graphs and the combination of these tools which give rise to several well known methods. We will also present some of the adaptive AMG techniques which construct iteratively better and better coarse spaces.


Instructor: Prof. Ludmil Zikatanov


Lecture 2. Introduction to Convolutional Neural Networks

Speaker: Zhanxing Zhu, Peking University

In this short tutorial, I will provide a comprehensive introduction to the convolutional neural networks (CNNs). CNNs are the most successful deep learning architectures and have been wildly used in many AI tasks and scientific domains. I will introduce (1) the basic components of CNNs, (2) popular architectures, including LeNet, AlexNet, ResNet, etc, (3) applications of CNNs in various tasks, (4) visualization of CNNs, (5) instability of CNN and (6) some regularization strategies. The tutorial requires some basic mathematical background and assumes no prior knowledge of machine learning or deep learning.


Instructor: Prof. Zhanxing Zhu

Teaching assistants: TBA



Lecture 3. An Integrated Introduction to Multigrid and Deep Learning

Speaker: Jinchao Xu, Penn State University

In this short course, an integrated introduction will be given to both multigrid methods and machine learning based on deep neural networks. The presentation will be elementary as it assumes little prior knowledge on both subjects and yet advanced as it will quickly reach to core issues in the relevant algorithm/model formulation, mathematical understanding and practical applications. Practice problems will be given to both theoretical analysis and practical applications that uses iFEM (for multigrid) and TensorFlow or Pytorch (for Deep Learning).


Instructor: Prof. Jinchao Xu

Teaching assistants: Juncai He and Xiaodong Jia (Peking University)


Reference: J. He and J. Xu, MgNet: A Unified Framework of Multigrid and Convolutional Neural Network

IMG2019, Aug 11--16, 2019

Online Registration: Click Here


Conference Venue: Howard Johnson, Kunming


Important Dates

MS Proposal Deadline

May 31, 2019


Early Registration Deadline

June 20, 2019


Conference Date

August 11-16, 2019


Conference Paper Submission

Oct 31, 2019

Submit at: CVS (use category “IMG 2019”)

Contact Us

For further information of the Conference, please contact the secretary of IMG2019.