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Scientific Computing and Data Science Seminar

We intend the terms scientific computing and data science to be broadly defined and inclusive. Topics of interest include but are not limited to:

  • High-performance computing, novel numerical methods, parallel algorithms, and application problems that involve numerical challenges.
  • Machine learning algorithms, deep learning and neural networks, applied/predictive modeling with real-world data, data-enabled science, dimensionality reduction, Bayesian methods, natural language processing, and computational statistics.

Our group is led by Harish Bhat, Tommaso Buvoli and Mayya Tokman.

During Fall 2019 we will meet on Tuesday at 10:00am-11:am in the Arts and Computational Sciences building (ACS) room 362C. If you are interested in giving a talk please select an empty time slot in our box note. To be added as an editor for the box file email or talk to Tommaso Buvoli. 

This seminar is part of the RTG theme “Scientific Computing and Data Science.” Graduate students should register for Math 298 (2 units) using CRN 37113;  the instructor of record for the course is Harish Bhat.

Schedule Fall 2019 (Still In Development - Check Back for updates)

  • Sept 3: Organizational Meeting
     
  • Sept 10: Alex Nguyen
    Title: Simulating Charged Particle Dynamics with Exponential Integrators
    Description: A critical component of the computational simulation of plasma and accelerated beam physics is solving for charged particle trajectories in electromagnetic fields - the so called particle pushing problem. In this talk we discuss a novel approach to particle pushing using exponential integrators, and report our findings. 
     
  • Sept 24: John Butcher (University of Auckland)
    Title: TBA
    Description: TBA
     
  • Oct 8: Cosmin Safta (Sandia National Laboratories)
    Title: TBA
    Description: TBA

Schedule Spring 2019

  • March 13th: Maxime Theliard (UC Merced)
    Title: The projection method for the incompressible Navier-Stokes equations
    Description: An introduction to the projection method for simulating incompressible newtonian fluids. We will both look at the standard method and how it can be implemented on adaptive grids to simulate single and two phase flows.
     
  • April 3rd: Tommaso Buvoli (UC Merced)
    Title: Time-Integration Techniques for Stiff Systems
    Description: An introduction to several types of time-integration techniques used to solve stiff systems arising from the discretization of partial differential equations.
     
  • April 17th: Changho Kim ( UC Merced)
    Title: Stochastic Differential Equations 101
    Description: An introductory lecture to SDEs and their numerical solutions will be presented. No prerequisite knowledge is assumed.
     
  • April 24th: Leonardo Zepeda-Núñez (UC Berkeley)
    Title: Fast and Scalable Algorithms for the High-Frequency Helmholtz Equation in 3D
    Description: There is much truth to the conventional wisdom that computational wave propagation is harder when the frequency is higher. Ten years ago, it was unclear that scalable sequential algorithms could even exist for the Helmholtz equation. Today, linear complexity is not only available in many scenarios of interest, but it is becoming clear that parallelism can take us much further. I will show recent results that indicate that genuinely sublinear parallel runtimes are possible in the 3D case, both with respect to the total number of unknowns, and the number of right-hand sides. ** Joint work with Laurent Demanet (MIT), Matthias Taus (MIT), Russell Hewett (Total), and Adrien Scheuer (UCL).
     
  • May 1st: Camille Carvalho (UC Merced)
    Title: Ad Hoc Finite element method for sign-changing Laplacian
    Description: In this presentation we will discuss how to use the finite element method for solving Laplace's equation in a partitioned domain (two materials for example). We will discuss the method, the error estimate, and how to design ad hoc meshes to ensure optimal convergence.