Irish Numerical Analysis Forum The 17th Workshop on Numerical Methods for Problems with Layer Phenomena was the first in this series to be held online instead of physically. Its success opened our eyes to the possibility of organising talks by speakers located in any part of the globe. Thus, in collaboration with other Irish researchers, we have now created the (virtual) Irish Numerical Analysis Forum which will include fortnightly seminars in all areas of numerical analysis that are aligned with the interests of the Irish numerical analysis community. Its aim will be to solicit lectures from leading international numerical analysts who will discuss their research area in a style that is accessible to most numerical analysts (i.e., not just those who are already familiar with the subject of the lecture). The seminar series started in January 2021. The talks will be streamed online via zoom and are free to view; one must however register in advance with INAF to gain access to them. We will usually have one talk every two weeks, but the talk timetable may vary from this. A registration puts you on our mailing list for receiving zoom links for all talks. To sign up for this seminar series and receive zoom links via email, please follow the link. If you experience any difficulties with your registration, you may contact Natalia.Kopteva@ul.ie. All seminar times are given in Dublin time; to convert them to your local time you may, for example, use the following Time Zone Converter.
Thu 2 December 2021, 14:00 (Dublin)
Gabriel Lord (Radboud University, Nijmegen)
Adaptive timestepping for S(P)DEs
Traditional explicit numerical methods to simulate stochastic differential equations (SDEs) or stochastic partial differential equations (SPDEs) rely on globally Lipschitz coefficients to ensure convergence. Many applications of interest include non Lipschitz drift functions. Implicit methods (when they exist) can often be too computationally expensive for practical uses and standard explicit methods suffer from growth of moments of the solution (which can be thought of as a form of numerical instability).
Therefore the construction of explicit methods to simulate SDEs or SPDEs with non-Lipschitz drift has been an area of great interest. These methods are broadly in a number of classes where either the numerical approximations are projected or the growth is controlled in the scheme.
In this talk we give an overview, emphasizing the context of SPDEs, of this issue and discuss how using an adaptive time step can be used to control this growth. We will discuss some of the key difficulties in proving strong convergence with a random time mesh and how these might be overcome. We show that in numerical experiments the adaptive time stepping is an efficient alternative to the other methods.
This work is joint with Conall Kelly (UCC) and Fandi Sun and Stuart Campbell (Heriot-Watt University)
Thu 13 January 2022, 14:00 (Dublin)
Georgios Akrivis (Institute of Applied and Computational Mathematics, FORTH, Heraklion, Crete, Greece)
The energy technique for BDF methods
The energy technique (or method) is probably the easiest way to establish stability
of parabolic (and other) differential equations.
The application of the energy technique to numerical methods with very good stability properties,
such as algebraically stable Runge-Kutta methods or A-stable multistep methods, is
straightforward. The extension to other numerical methods, such as A($\vartheta$)-stable methods,
requires some effort and is more interesting; the main difficulty concerns suitable choices of test functions.
Here we focus on the energy technique for backward difference formula (BDF) methods.
In the cases of the A-stable one- and two-step BDF methods the application is trivial.
The energy technique is applicable also to the A($\vartheta$)-stable three-, four- and
five-step BDF methods via Nevanlinna--Odeh multipliers.
The main results are:
+ No Nevanlinna--Odeh multipliers exist for the six-step BDF method.
+ The energy technique is applicable under a relaxed condition on the multipliers.
+ We present multipliers that make the energy technique applicable also to the six-step BDF method.
Besides its simplicity, the energy technique is powerful, it leads to several stability estimates,
and flexible, it can be easily combined with other stability techniques.
The talk is based on the paper:
G. A., M. Chen, F. Yu, Z. Zhou: The energy technique for the six-step BDF method,
SIAM J. Numer. Anal., 59 (2021), 2449-2472.
Joint work with:
Minghua Chen, Fan Yu: School of Mathematics and Statistics, Gansu Key Laboratory of Applied Mathematics
and Complex Systems, Lanzhou University, Lanzhou, P.R. China;
Zhi Zhou: Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong.
- Thu 27 January 2022, 14:00 (Dublin) Evelyn Buckwar (Johannes Kepler University Linz, Austria)
- Thu 10 February 2022, 14:00 (Dublin) Johnny Guzman (Brown University, USA)
Thu 18 November 2021, 14:00 (Dublin)
Andy Wathen (University of Oxford)
Parallel preconditioning for time-dependent PDEs
Large scale simulations with partial differential equations (PDEs) demand significant computational resources---it is one of the problem areas where parallel computation is often a necessity. Ideas for parallel solution of stationary PDE problems have been widely explored, with Domain Decomposition preconditioning being a leading technique. For evolutionary PDEs, however, it would seem that causality---the need for the solution at earlier time in order to realise it later---should severly limit possibilities for effective parallel computation over time.
In this talk we develop one possible `parallel-in-time' approach that involves ideas and algorithms from numerical linear algebra.
Thu 4 November 2021
Kirk Soodhalter (Trinity College Dublin)
Analysis of block GMRES using a new *-algebra-based approach
In this talk, we give an general overview of a class of iterative methods for solving linear systems called Krylov subspace methods (KSM) and their block generalizations, focusing on the Generalized Minimum RESidual method (GMRES). We then discuss the challenges of extending convergence results of classical GMRES to its block counterpart and propose a new approach to this analysis.
Block KSMs such as block GMRES are generalizations of classical KSMs, and are meant to iteratively solve linear systems with multiple right-hand sides (a.k.a. a block right-hand side) all-at-once rather than individually. These methods have regained attention recently due in part to the computational advantages they offer in the high-performance computing setting. However, this all-at-once approach has made analysis of these methods more difficult than for classical KSMs because of the interaction of the different right-hand sides.
We have proposed an approach built on interpreting the coefficient matrix and block right-hand side as being a matrix and vector over a *-algebra of square matrices. This allows us to sequester the interactions between the right-hand sides into the elements of the *-algebra and extend some classical GMRES convergence results to the block setting. We then discuss some challenges which remain and some ideas for how to proceed.
Thu 21 October 2021
Michal Križek (Czech Academy of Sciences, Prague)
Finite element approximation of a nonlinear heat conduction problem in anisotropic media
This lecture will be a survey of results which we have obtained in solving a stationary nonlinear heat condiction problem by the finite element method. In particular, we present uniqueness theorems for the classical and weak solutions, a comparison theorem, existence theorems for the weak and finite element solutions, convergence of finite element approximations without any regularity assumptions, a priori error estimates, numerical integration, discrete maximum principle, and nonlinear radiation boundary conditions.
Thu 7 October 2021
Alan Demlow (Texas A&M University)
Geometric errors in surface finite element methods
The Laplace-Beltrami problem is widely used to model physical and other phenomena on surfaces. Finite element methods a widely used option to solve such equations. Typically in FEM the surface on which the PDE is posed is first approximated by a nearby discrete surface, and then the finite element method is posed on the discrete surface much as for PDE on Euclidean spaces. Replacing the original continuous surface by a discrete approximation gives rise to a consistency error (variational crime) that is typically called a geometric error. Until a few years ago, instances of geometric errors appearing in the literature exhibited consistent behavior across a range of situations. More recently a number of cases have arisen in which more subtle behavior occurs. We will describe a couple of such cases, those of eigenvalue problems and a posteriori error estimation.
Thu 23 September 2021
Thomas Apel (Universität der Bundeswehr München)
A pressure-robust discretization of the
Stokes problem on anisotropic meshes
(joint work with Volker Kempf)
Anisotropic finite element meshes are particularly efficient when the
solution of the problem has anisotropic features like boundary layers
or edge singularities. Pressure-robustness means that the velocity
discretization error does not depend on the pressure approximation.
The Crouzeix-Raviart method is known to be inf-sup stable on arbitrary
anisotropic meshes, yet not pressure-robust.
Inf-sup stable finite element schemes with discontinuous pressure can
be made pressure-robust by a modified discretization of the exterior
forcing term using H(div)-conforming reconstruction operators like the
Raviart-Thomas or Brezzi-Douglas-Marini interpolants, .
In order to show that the reconstruction approach works for
anisotropic discretizations, it was necessary to investigate the
interpolation error for the Raviart-Thomas or Brezzi-Douglas-Marini
interpolants on anisotropic elements disclosing subtleties in the
definition of the spaces and in shape assumptions, [2,3].
In collaboration with Alexander Linke and Christian Merdon we have
generalized the modified Crouzeix-Raviart method to a large class of
anisotropic triangulations. Numerical examples confirm the theoretical
 A. Linke: On the role of the Helmholtz decomposition in mixed
methods for incompressible flows and a new variational
crime. Comput. Methods Appl. Mech. Engrg. 268(2014), 782-800.
 G. Acosta, Th. Apel, R. G. Durán, A. L. Lombardi: Error estimates
for Raviart-Thomas interpolation of any order on anisotropic
tetrahedra. Math. Comp. 80(2011), 141-163.
 Th. Apel, V. Kempf: Brezzi-Douglas-Marini interpolation of any
order on anisotropic triangles and tetrahedra. SIAM J. Numer. Anal.
 Th. Apel, V. Kempf, A. Linke, Chr. Merdon: A nonconforming
pressure-robust finite element method for the Stokes equations on
anisotropic meshes. IMA Journal of Numerical Analysis (2021).
 Th. Apel, V. Kempf: Pressure-robust error estimate of optimal
order for the Stokes equations: domains with re-entrant edges and
anisotropic mesh grading. Calcolo 58(2021).
Thu 9 September 2021
Scott MacLachlan (Memorial University of Newfoundland)
Finite-Element Modeling of Liquid Crystal Equilibria
Numerical simulation tools for fluid and solid mechanics are often
based on the discretisation of coupled systems of partial differential
equations, which can easily be identified in terms of physical
conservation laws. In contrast, equilibrium configurations of many
liquid crystal phases are more naturally described by the first-order
optimality conditions of constrained free-energy functionals. In
this talk, I will present a variational finite-element approach for
computing liquid crystal equilibria, and demonstrate its use for both
nematic (rod-like) and smectic (soap-like) liquid crystals. As the
main scientific and engineering interest in liquid crystals comes from
their ability to exhibit multiple distinct stable equilibrium states,
I will discuss the combination of this framework with a nonlinear
deflation technique that allows discovery of the energy landscapes for
Slides + Videos
Thu 12 August 2021
Carmen Rodrigo (Universidad de Zaragoza)
Robust discretizations and solvers for poroelastic problems
The numerical simulation of poroelastic problems has received a lot of attention due to their wide range of applications. Geothermal energy extraction, CO2 storage, hydraulic fracturing or cancer research are among typical societal relevant applications of poromechanics. Robust discretizations with respect to all the physical parameters are needed for this type of problems to obtain reliable numerical solutions. This is a very important task and some efforts are being carried out in this address by the scientific community. In particular, we present here a stable discretization for the three field formulation of the poroelasticity problem. In addition, intensive research has also been focused on the design of efficient methods for solving the large linear systems arising from the discretization of Biot's model, since in real simulations it is the most consuming part. We aim to design efficient and robust preconditioners to accelerate the convergence of Krylov subspace methods. The proposed block preconditioners for solving the Biot's model are based on the well-posedness of the obtained discrete systems, and are robust with respect to both physical and discretization parameters. Numerical results are presented to support the theoretical results.
Thu 1 July 2021
Erin Carson (Charles University in Prague)
The cost of iterative computations at scale
With exascale-level computation on the horizon, the art of predicting
the cost of computations has acquired a renewed focus. This task is
especially challenging in the case of iterative methods, for which a
realistic convergence rate often cannot be determined with certainty a
priori (unless we are satisfied with potentially outrageous
overestimates) and which typically suffer from performance bottlenecks
at scale due to synchronization cost. Moreover, the amplification of
rounding errors can substantially affect the practical performance, in
particular for methods with short recurrences.
In this talk, we focus on what we consider to be key points which are
crucial to understanding the cost of iteratively solving linear
algebraic systems, particularly in the context of Krylov subspace
methods and their communication-avoiding variants. We argue that
achieving optimal performance in practice will require a holistic
approach, involving collaboration between the fields of numerical
analysis, computer science, and computational sciences.
Thu 17 June 2021
Kai Diethelm (University of Applied Sciences Würzburg-Schweinfurt)
Numerical Methods for Terminal Value Problems of Fractional Order
Traditionally, ordinary differential equations of fractional order $\alpha \in (0,1)$ are considered in combination with initial conditions, i.e.\ one imposes a condition on the unknown function at the starting point $a$, say, of the fractional differential operator in question. In practical applications, however, it is not always possible to provide the information about the unknown solution at this particular point. Rather, one is sometimes forced to use a condition of a form like $y(b) = y^*$ with some $b > a$. We briefly discuss analytic properties of such problems, in particular the questions of existence and uniqueness of their solutions. The main part of the talk will then be devoted to numerical methods for obtaining approximate solutions to problems of this type.
Thu 3 June 2021
Bosco Garcia-Archilla (University of Seville)
Stabilized Finite Element Methods for the Navier-Stokes Equations
This talk will be a journey for non experts on the error analysis of finite element discretizations of the Navier-Stokes equations. We will start with the error analysis of the heat equation, and, step by step, we will add convection, compressibility and nonlinearity until reaching the Navier-Stokes equations. On each of these steps, we will analyse the effect of small diffusion on the error bounds, paying special attention to the reduction in order of convergence. Also we will comment on how the stabilization terms (terms added to the discretization improve the approximation) counterbalance the effect of small diffusion. Numerical examples will illustrate the different elements of the analysis.
Thu 20 May 2021
Zhimin Zhang (Beijing Computational Science Research Center)
Superconvergence: An Old Field with New Territories
The phenomenon of superconvergence is well understood for the h-version finite element method, and researchers in this old field have accumulated a vast literature during the past 50 years. However, a similar study for other numerical methods such as the p-version finite element method, spectral methods, discontinuous Galerkin methods, and finite volume methods is lacking. We believe that the scientific community would also benefit from the study of superconvergence phenomena for those methods. In recent years, some efforts have been made to expand the territory of superconvergence analysis. In this talk, we present some recent developments in superconvergence analysis for discontinuous Galerkin methods and polynomial spectral methods. At the same time, some current issues and unsolved problems will also be addressed.
Thu 6 May 2021
Catherine Powell (University of Manchester)
Adaptive Stochastic Galerkin Approximation for Parameter-Dependent PDEs
In this talk, we discuss numerical analysis aspects of stochastic Galerkin approximation for performing forward uncertainty quantification (UQ) in PDE models with uncertain (or parameter-dependent) inputs. Starting with a scalar elliptic test problem, we first describe a general strategy for performing a posteriori error estimation to drive adaptive solution algorithms. We then discuss how this methodology can be extended to a more challenging linear elasticity problem with uncertain Young’s modulus. We introduce a three-field parameter-dependent PDE model and develop an adaptive stochastic Galerkin mixed finite element scheme. We estimate the error in the natural weighted norm with respect to which the weak formulation is stable. Exploiting the connection between this norm and the underlying PDE operator also leads to an efficient block-diagonal preconditioning scheme for the associated discrete problems. Both the error estimator and the preconditioner are provably robust in the incompressible limit. If time allows, we will also discuss recent work for poroelasticity problems.
Thu 22 April 2021
Ricardo Durán (University of Buenos Aires)
The Stokes equations with singular boundary data
First we recall some classic results on the well posedness and numerical
approximation of the Stokes equations, particularly we present the fundamental inf-sup condition and the Bogovskii's constructive approach to prove
it. Usually the theory is presented for the homogeneous Dirichlet problem
but, by standard trace results, it can be extended to treat non-homogeneous
boundary data provided they are enough regular.
Then, we consider the Dirichlet problem with singular data and analyze
its finite element approximation. We prove quasi-optimal error estimates for
data in fractional order Sobolev spaces approximating the boundary datum
by appropriate regularizations, or by the Lagrange interpolation when it is
A typical example used to test numerical methods is the so called lid-driven cavity problem. Our general results give almost optimal order error
estimates for this case when quasi-uniform meshes are used.
Finally we comment on an a posteriori error estimator and present some
numerical examples showing the good performance of an adaptive procedure
based on it.
Thu 8 April 2021
Emmanuil (Manolis) Georgoulis (University of Leicester/National Technical University of Athens)
hp-Version discontinuous Galerkin methods on arbitrarily-shaped elements
We extend the applicability of the popular interior-penalty discontinuous Galerkin (dG) method discretizing advection-diffusion-reaction problems to meshes comprising extremely general, essentially arbitrarily-shaped element shapes. In particular, our analysis allows for curved element shapes, without the use of nonlinear elemental maps. The feasibility of the method relies on the definition of a suitable choice of the discontinuity penalization, which turns out to be explicitly dependent on the particular element shape, but essentially independent on small shape variations. This is achieved upon proving extensions of classical inverse estimates to arbitrary element shapes. These inverse estimates may be of independent interest. A priori error bounds for the resulting method are given under very mild structural assumptions restricting the magnitude of the local curvature of element boundaries. We further discuss the applicability of this new framework within adaptive algorithms and discuss briefly the proof of a posteriori error bounds.
Thu 25 March 2021
Gunar Matthies (Technical University Dresden)
Local projection stabilisation
Originally proposed to stabilise equal-order discretisations of the Stokes problem, local projection stabilisation (LPS) has been applied successfully stabilise dominating convection in both convection-diffusion equations and incompressible flow problems.
The first part will consider convection-diffusion equations and discuss the role of a special interpolation operator that is used in the numerical analysis. We will give conditions that ensure its existence and present some example settings. Numerical results will illustrate the behaviour of local projection stabilsation.
The second part of the talk will present some results for Oseen problems where we consider both equal-order and inf-sup stable discretisations. We will give also some numerical results.
Thu 11 March 2021
Ivan Graham (University of Bath)
Solving the Helmholtz equation at high frequency
The Helmholtz equation arises when the linear wave equation is reduced
to a steady state PDE via Fourier transform in time. It is arguably
one of the simplest equations describing linear waves in general
geometries and media, and it provides a scalar model for more complicated
problems like the elastic wave equation or Maxwell's equations. It
arises in many applications, including inverse problems e.g., seismic
imaging. Despite it's linearity and apparent simplicity, this
equation is difficult to solve because (a) its stability properties
are complicated and depend on domain geometry and material properties of the
medium; (b) at high frequency, solutions are highly oscillatory,
very fine meshes are needed to even guarantee the existence/uniqueness of
numerical solutions, and finer meshes are needed for accuracy; (c) the
system matrices which arise after discretization are highly indefinite
and non-normal, and (in contrast to positive definite PDE problems),
the formulation and analysis of fast parallel iterative methods is
difficult. On the last point, there is currently intense research
interest amongst a number of groups worldwide on developing efficient
In the talk I'll give some background to the Helmholtz problem, describe
what is known about its stability and finite element error analysis
and then I'll describe work I have been doing with colleagues on the
formulation and analysis of domain decomposition methods for solving
the linear systems arising from discretized Helmholtz problems. My
main collaborators for the talk are Shihua Gong and Euan Spence (both
of Bath) and Jun Zou (Chinese University of Hong Kong), although other
collaborators will also be mentioned during the talk.
Thu 25 February 2021
Bangti Jin (University College London)
Numerical methods for time-fractional diffusion
During the past decade, parabolic type equations involving a fractional-order derivative in time have received much attention, and several numerical methods have been developed. Many existing methods are developed by assuming that the solution is sufficiently smooth. In this talk, I will describe some works on developing robust numerical schemes that do not assume solution regularity directly, but only data regularity.
Thu 11 February 2021
Gabriel Barrenechea (University of Strathclyde)
The discrete maximum principle in finite element methods
In this talk the satisfaction of the discrete maximum principle for the
finite element method will be reviewed. Starting from the most basic results
on the topic, and basing ourselves in the algebraic equations, sufficient conditions
for the satisfaction of the discrete maximum principle for nonlinear discretisations
(of shock-capturing kind) will be given. As an example of such discretisations
the family of algebraic flux correction schemes will be analysed in the case of
the convection-diffusion equation, where the role of the mesh geometry will
Thu 28 January 2021
Abner Salgado (University of Tennessee)
Numerical methods for spectral fractional diffusion
We present and analyze finite element methods (FEMs) for the numerical approximation of the spectral fractional Laplacian. This method hinges on the extension to an infinite cylinder in one more dimension. We discuss rather delicate numerical issues that arise in the construction of reliable FEMs and in the a priori and a posteriori error analyses of such FEMs for both steady, and evolution fractional diffusion, both linear and nonlinear. We show illustrative simulations, applications, and mention challenging open questions.
Thu 21 January 2021
Patrick Farrell (University of Oxford)
Reynolds-robust preconditioners for the stationary incompressible Navier-Stokes equations
When approximating PDEs with the finite element method, large sparse linear
systems must be solved. The ideal preconditioner yields convergence that is
algorithmically optimal and parameter robust, i.e. the number of Krylov
iterations required to solve the linear system to a given accuracy does not grow
substantially as the mesh or problem parameters are changed.
Achieving this for the stationary Navier-Stokes has proven challenging: LU
factorisation is Reynolds-robust but scales poorly with degree of freedom count,
while Schur complement approximations such as PCD and LSC degrade as the
Reynolds number is increased.
Building on the work of Schöberl, Olshanskii, and Benzi, in this talk we present
the first preconditioner for the Newton linearisation of the stationary
Navier--Stokes equations in three dimensions that achieves both optimal
complexity and Reynolds-robustness. The scheme combines augmented Lagrangian
stabilisation, a custom multigrid prolongation operator involving local solves
on coarse cells, and an additive patchwise relaxation on each level that
captures the kernel of the divergence operator.
We present 3D simulations with over one billion degrees of freedom with robust
performance from Reynolds number 10 to 5000. We also present recent extensions
to implicitly-constituted non-Newtonian problems, and to magnetohydrodynamics.