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  Model order reduction, numerical linear algebra

Scientific Computing Group

Scientific Computing is a fast-growing, highly interdisciplinary field that brings together methods from numerical analysis, high-performance computing and various application fields. It is the area of research that provides better simulation tools aimed at many different applications.
Many of our current application areas are energy-related:

  • Wind-turbine and wind-farm aerodynamics,
  • Tokamak magnetohydrodynamics,
  • Plasma-assisted combustion,
  • Battery technologies,
  • Porous media flows,
  • Electronic circuits,
  • Bio-fuels,
  • Lighting.
Other application areas to which our research is directed are:
  • Fluid-structure interactions,
  • Hypervelocity impacts,
  • Shape optimization.
Numerical methods on which we currently do research:
  • Symplectic time-integration methods,
  • Physics-compatible finite-volume methods (exponential schemes, multi-D upwind schemes, limiters, ...),
  • Generalized Krylov methods,
  • Evolutionary design algorithms.

Some of the active research in more detail

Complete flux based finite volume schemes
Many applications give rise to mathematical models with partial differential equations of advection-diffusion-reaction type. These equations describe conservation of mass, momentum, energy and alike. Applications we work on include combustion and plasma physics.
A possibility for the computation of numerical fluxes in these applications is to use an exponential scheme. These schemes have the same accuracy for all Reynolds numbers in flow problems. Furthermore, exponential schemes preserve monotonicity properties of the exact solution.

Smoothed particle hydrodynamics (SPH)
Smoothed Particle Hydrodynamics (SPH) is a numerical method that is successfully applied in science and engineering. Since it is a gridless, particle-based technique, it is especially suited for treating problems with free surfaces, multiphase flow, high-velocity impact, crack propagation and large deformations.
SPH is a Lagrangian method in which the state of the system is represented by a set of particles. These interact with their neighbors; the range of interaction is controlled by a smooth kernel function. Field variables such as density, velocity and acceleration are calculated from the particle distribution. SPH is easy to implement. The resulting equations can be solved in a straightforward manner and allow massive parallelization.

Indefinite systems
The numerical approximation of many scientific and engineering problems leads to block-structured indefinite linear systems in saddle point form. These systems originate from for instance conservation laws, constrained optimization problems, mixed finite element discretizations and generalized least squares problems. The successful design of robust, scalable and efficient preconditioners is intimately connected with an understanding of the structure of the resulting block matrix system and relies heavily on exploiting this structure.

Model order reduction (MOR)
MOR is a method to capture dominant features of problems, enabling much faster simulation. It aids realistic simulations of multi-aspect problems. Special algorithms have been developed for coupled problems, differential-algebraic systems, dominant pole algorithms, nonlinear oscillators and more.

Inverse and ill-posed problems
Many problems in science and engineering lead o inverse problems that may be ill-posed: there are many solutions, or the solution may be very sensitive with respect to change in the input data. We develop efficient generic methods for these problems, for instance based on new and improved variants of Tikhonov regularization.

Generalized Krylov methods
Krylov methods are very suitable techniques for solving important large-scale linear algebra problems such as linear systems, eigenvalue problems, and model reduction. We develop new variants of Krylov methods that are tailored to solve challenging problems.

Inner-outer methods
Large nonlinear problems are often solved by Newton methods. In these Newton methods a large system has to be solved in every step, which is computationally expensive. We propose methods to solve these linear systems inexactly, so that the solution is obtained faster.

© Centre for Analysis, Scientific Computing and Applications. For questions please refer to the editor.
This page modified: Tue Mar 04 13:14:11 CET 2014