MOOC-HPFEM online course at KTH

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MOOC-HPFEM online course at KTH

Learn High Performance Finite Element Modeling from top researchers at KTH Royal Institute of Technology

Free Online Course starting in September 2017 at www.edX.org

 

Learn how to make cutting edge simulations! Engineering simulations
are rapidly becoming fundamental in virtually all industrial sectors,
from medicine to energy, aerospace and beyond. The breakthrough
general adaptive finite element methods (AFEM) and open source FEniCS
software you will learn in this course will position you to take lead
to effectively solve the grand challenges in science and
engineering. The course will be open for enrollment in August at edX
(http://www.edx.org)

Top participant performances will be awarded access to a supercomputer
and more advanced simulations of turbulent flow.

Target groups

The course targets engineering students who have passed the second or
third year, and engineers in industry. Anyone who has a basic
knowledge of linear algebra and calculus should be able to absorb most
of the material in the course.

What you will learn

You will learn how to model general partial differential equations
(PDE) with the finite element method (FEM) in an automated abstract
software framework. In this course the open source framework used is
FEniCS and FEniCS-HPC
(http://fenicsproject.org
and http://fenics-hpc.org), with
scalable performance.

More specifically, after completing the course you will be able to:

  1. derive adaptive finite element methods for general PDE with relevance
    in industry: the Navier-Stokes equations for incompressible flow, the
    wave equation, linear elasticity, and multi-physics combinations of
    these equations.
  2. derive fundamental properties of the methods, which are key for
    robustness and efficiency, such as: energy conservation, stability,
    and a priori and a posteriori error estimates.
  3. account for general FEM-algorithms such as assembly, adaptvity and
    local mesh refinement and have a basic understanding of their
    implementation in FEniCS-HPC.
  4. account for parallel data structures and algorithms for
    distributed memory architectures in a general FEM-framework and
    inspect their implementation in FEniCS-HPC: dsitributed computational
    mesh, ghost entities, distributed sparse linear algebra, local mesh
    refinement by bisection for a distributed computational mesh and
    general goal-oriented adaptive error control.
  5. use a general framework, such as FEniCS-HPC, to model and solve
    general PDE on a supercomputer.

Teachers

  • Course coordinator Asst. Prof. Johan Jansson, KTH and BCAM (jjan@kth.se)
  • Prof. Johan Hoffman, KTH
  • Dr. Niclas Jansson, KTH
  • PhD Cand. Massimiliano Leoni, KTH and BCAM
  • PhD Cand. Niyazi Cem Degirmenci, BCAM and KTH
  • Dr. Van Dang Nguyen, KTH

[1] Picture and simulation data by: Jonas Thorén, Jeannette Spũhler, Johan Jansson and PHILIPS