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CAM Seminar

Computational and Applied Mathematics Seminar

Fall 2022

Mondays at 2:10-3:00p.m  (ZOOM/WebEx or in-person (Room Carver 401) talks)

The CAM Seminar is organized in the ISU Mathematics Department. It brings speakers from inside and outside of ISU, raising issues and exchanging ideas on topics of current interest in the are of computational and applied mathematics.

 

 

 


  • September 19

       Title:  General introduction/open discussion on data science   

        Hailiang Liu and Eric Weber, Iowa State University

Abstract:  An introduction on data sciences and research opportunities will be given.

TrAC info: TrAC Activities Summary.pptx,

TrAC link

TADS info: TADS link

ZOOM Link:

Join from a PC, Mac, iPad, iPhone or Android device:

    Please click this URL to start or join. https://iastate.zoom.us/j/92463082326?pwd=d3FocXFCUUlCL0YvUEFkYnNHc3EzZ…
    Or, go to https://iastate.zoom.us/join and enter meeting ID: 924 6308 2326 and password: 123456
 
Join from dial-in phone line:

    Dial: +1 646 931 3860 or +1 301 715 8592
    Meeting ID: 924 6308 2326
    Participant ID: Shown after joining the meeting
    International numbers available: https://iastate.zoom.us/u/abCRUfLjPk


  • September 26

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  • October 03

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  • October 10

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        Yanzhi Zhang, Missouri University of Science and Technology

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  • October 17

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        Xiaokai Huo, Iowa State University

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  • October 24

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  • October 31

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  • November 07

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  • November 14

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  • November 28

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        James Rossmanith, Iowa State University

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  • August 22 

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  • August 29 (ROOM 401,  2:10-3:00p.m)

       Title: Cell-average based neural network fast solvers for time dependent partial differential equations      

        Jue Yan, Iowa State University

Abstract:   In this talk, we present the recently developed machine learning cell-average based neural network (CANN) method for time dependent problems. The method is motivated by finite volume scheme and is based on the integral or weak formulation of the PDEs. A simple feed forward network is applied to learn the solution average difference between two neighboring time steps. Well trained network parameter can be interpreted as the scheme coefficients of an explicit one-step time discretization method. While convergence orders are observed, quite a few unusual properties are found with the method that are not common to conventional numerical methods. CANN method is found being able to be relieved from explicit scheme CFL restriction. A much larger time step size $dt=O(dx)$ can be applied to evolve the solution forward in time. Thus the network method is extremely fast and efficient. CANN method can sharply evolve contact discontinuity, shock and rarefaction waves for nonlinear hyperbolic conservation laws. The method has been successfully applied to high dimensional parabolic PDEs and third-order and fifth-order KdV type equations.

 


  • September 05 (Labor Day)

 

  • September 12 (ROOM 401,  2:10-3:00p.m)

       Title:  Organizational Meeting     

Abstract:   We will hold an organizational meeting to discuss the scope, goals and objectives of CAM seminar.  

ZOOM Link: