Erika Hausenblas

NA
4papers
53citations
Novelty22%
AI Score16

4 Papers

NAMar 25, 2013
Convergence analysis of sectional methods for solving aggregation population balance equations: The fixed pivot technique

Ankik Kumar Giri, Erika Hausenblas

In this paper, we introduce the convergence analysis of the fixed pivot technique given by S.Kumar and Ramkrishna \cite{Kumar:1996-1} for the nonlinear aggregation population balance equations which are of substantial interest in many areas of science: colloid chemistry, aerosol physics, astrophysics, polymer science, oil recovery dynamics, and mathematical biology. In particular, we investigate the convergence for five different types of uniform and non-uniform meshes which turns out that the fixed pivot technique is second order convergent on a uniform and non-uniform smooth meshes. Moreover, it yields first order convergence on a locally uniform mesh. Finally, the analysis exhibits that the method does not converge on an oscillatory and non-uniform random meshes. Mathematical results of the convergence analysis are also demonstrated numerically.

NAMay 2, 2018
Time-discretization of stochastic 2-D Navier--Stokes equations with a penalty-projection method

Erika Hausenblas, Tsiry Randrianasolo

A time-discretization of the stochastic incompressible Navier--Stokes problem by penalty method is analyzed. Some error estimates are derived, combined, and eventually arrive at a speed of convergence in probability of order 1/4 of the main algorithm for the pair of variables velocity and pressure. Also, using the law of total probability, we obtain the strong convergence of the scheme for both variables.

NADec 18, 2018
Pathwise space approximations of semi-linear parabolic SPDEs with multiplicative noise

Sonja Cox, Erika Hausenblas

We provide convergence rates for space approximations of semi-linear stochastic differential equations with multiplicative noise in a Hilbert space. The space approximations we consider are spectral Galerkin and finite elements, and the type of convergence we consider is strong and almost sure uniform convergence, i.e., pathwise convergence. The proofs are based on a previously published perturbation result for such equations.

NASep 27, 2018
Numerical approximation of stochastic evolution equations: Convergence in scale of Hilbert spaces

Hakima Bessaih, Erika Hausenblas, Tsiry Randrianasolo et al.

The present paper is devoted to the numerical approximation of an abstract stochastic nonlinear evolution equation in a separable Hilbert space {$\mathrm{H}$}. Examples of equations which fall into our framework include the GOY and Sabra shell models and { a class of nonlinear heat equations.} The space-time numerical scheme is defined in terms of a Galerkin approximation in space and a { semi-implicit Euler--Maruyama scheme in time}. {We prove the convergence in probability of our scheme by means of an estimate of the error on a localized set of arbitrary large probability.} Our error estimate is shown to hold in a more regular space $\mathrm{V}_β\subset \mathrm{H}$ with $β\in [0,\frac14)$ and { that the explicit rate of convergence of our scheme depends on this parameter $β$. }