2021 Spring Meeting

Participants of the 2019 Springmeeting at Campus Groenenborger in Antwerp

Friday May 28th, 2021, the Dutch-Flemish Scientific Computing Society is organizing its annual spring meeting, together with the Technical University in Delft the Netherlands. Last years meeting was cancelled but we will do all we can to make the 2021 meeting happen. If the measurements allow it, we hope to see all of you in Delft, if not, we will create a hybrid or fully online meeting.
We have asked last years speakers if they want to present their research at this years meeting, luckily some already confirmed.

This year, participation including lunch will be free. Registration is not possible yet.
No shows will be charged with 25 euros administration costs.


TU Delft, Gebouw 26 (Zaal Kubus, A.0.350)
Van der Burghweg 1
2628 CS Delft

Directions public transport:

De Bouwcampus can be reached from Delft Central Station by bus 69,40 and 174. Bus stops close to De Bouwcampus are TU- Sport en Cultuur and TNO Zuidpolder.
From Delft-Zuid station you can walk to De Bouwcampus in about 20 minutes.

Directions by car and parking

Coming from the A13 take exit 10 Delft Zuid and follow the signs TU Delft. Go right in to the Schoemakerstraat and at the first crossing go left. If you then turn left again you are on a free parking spot named “Sports”, there are 300 spots there. It is a 5 minute walk from this parking to Van der Burghweg 1.
In the direct area of the University there is limited parking space available, you risk a fine if you are not parked in the designated field.

Program 2021

09:00-09:30 Registration, coffee and tea

09:30-10:10 Kristof Cools (TU Delft)

10:10-10:35 Marieke Kootte (TU Delft)

10:35-11:00 Yuan Hou (University of Antwerp)

11:00-11:30 Coffee and tea

11:30-11:55 Kelbij Star (Ghent University/SCK CEN)

11:55-12:20 Harshit Bansal (TU Eindhoven)

12:20-12:30 Group picture

12:30-13:30 Lunch

13:30-13:55 Speaker

13:55-14:20 Speaker

14:20-14:50 Coffee and tea

14:50-15:15 Speaker

15:15-15:50 Matthias Schlottbom (University of Twente)

15:50-16:00 Closure


Speakers Spring meeting SCS 2021

   Kristof Cools: In 2018, Dr. Cools joined the Department of Industrial and Applied Mathematics at TU Delft. His research interests include the spectral properties of the boundary integral operators of electromagnetics, stable and accurate discretization schemes for frequency and time domain boundary element methods, domain decomposition techniques, and on the implementations of algorithms from computational physics for high-performance computing.
Marieke Kootte: received her B.Sc. degree in civil engineering in 2014 at Delft University of Technology, The Netherlands. She then obtained her double degree in applied mathematics at Delft University of Technology and KTH Stockholm, Sweden, in 2017.
Currently she is pursuing her Ph.D. degree at the Numerical Analysis research group of the Delft Institute of Applied Mathematics, Delft University of Technology. Her topic focuses mainly on fast and robust power flow solvers for integrated transmission-distribution networks and partly on defining optimal bid strategies for different electricity markets.
Yuan Hou: obtained his BSc and MSc degrees in Software Engineering from East China Normal University in Shanghai, China, in 2014 and 2017 respectively. Since October 2017, he is a PhD student in the Computational Mathematics (CMA) research group at the University of Antwerp in Belgium. His research focuses on using state‐of‐art multivariate exponential analysis techniques to tackle challenges in identified computational science and engineering applications.
 Kelbij Star studied Applied Physics at the Delft University of Technology. Her master was mainly focused on transport phenomena and fluid flow. After having worked in the industry for one year, she started a PhD at Ghent University under the supervision of professor Degroote in October 2017. The PhD is in collaboration with SCK CEN, a nuclear research institution in Belgium. She joined SISSA’s mathLab group in Italy for a research stay during the first half of her second year. In addition, she visited the Scientific Computing group at CWI for a three-month internship program in her third year of the PhD. Her PhD is about developing reduced order models for fluid dynamics problems.
 Harshit Bansal: pursued the dual-degree program in Mechanical Engineering at the Indian Institute of Technology, Kharagpur, India. He obtained Bachelor of Technology in Mechanical Engineering and Master of Technology in Mechanical Engineering with specialization in Mechanical Systems Design in 2016. After finishing his masters, he started his PhD in September 2016 (under the guidance of Prof. dr. Wil Schilders, Prof. dr. ir. Nathan van de Wouw and Dr. Laura Iapichino) in the Centre for Analysis, Scientific Computing and Applications (CASA) at the Department of Mathematics and Computer Science, Eindhoven University of Technology, The Netherlands. He is carrying out his PhD under the aegis of Shell-NWO/FOM Programme in Computational Sciences for Energy Research. The main focus of the project is to develop structure-preserving model order reduction techniques in the scope of drilling automation. His research interests include model order reduction of transport-dominated problems, port-Hamiltonian modelling, structure-preserving discretization and model order reduction of distributed parameter systems.
  Matthias Schlottbom obtained a PhD in mathematics (Dr. rer. nat.) at the RWTH Aachen in 2011.
After that he was PostDoc at the technical universities in Munich and Darmstadt and at the WWU Münster. In 2016 he joined the Department of Applied Mathematics at the University of Twente where he is an Associate Professor now. His research focuses on the development and analysis of numerical methods for partial differential equations and inverse problems. Areas of applications range from medical imaging and biology to photonic crystals.


2021 Abstracts 

Marieke Kootte
Numerical Assessment of Integrated Transmission-Distribution Electricity Networks
Integrated electricity network models are necessary to represent the power flow within modern electricity systems accurately. Conventional models are designed to work on separated transmission and distribution networks only, but the continuing growth of electricity consumption, demand side participation, and renewable resources makes the electricity networks co-dependent. Integrated models incorporate the coupling of the networks and model the influence that the networks have on each other.  
In this talk, we share several methods to create integrated network models. Furthermore, we compare and assess the numerical performance of the methods, which are convergence rate and CPU-time, on several test networks ranging from 50 to 25000 unknowns.  We show the effect of the amount of imbalance at distribution level on transmission networks, that is evoked by highly variable resources and loads installed along the distribution network.
Yuan Hou: 

Applications and analysis of exponential models

Based on the latest development in exponential analysis, we present a new method that can extract high resolution information from noisy data. In comparison to other methods, our method offers a range of advantages. The method neither suffers the curse of dimensionality nor requires a prior estimate of the number of spectral peaks. It can work with sub-Nyquist sampled data and offers a validation step, which is very useful in low SNR conditions. A favourable computation cost results from the fact that several independent smaller systems are solved instead of one large system incorporating all measurements simultaneously.

We apply the proposed method to real world applications. In imaging, we explore the method for texture classification and defect detection. In 3-dimensional space, we extract the location information of scattering centers from inverse synthetic aperture radar (ISAR) noisy data.

Kelbij Star:
Reduced order modeling for computational fluid dynamics problems with parametric boundary conditions
Complex fluid dynamics problems are usually solved numerically using discretization methods such as the finite volume method. Boundary conditions are essential for defining these numerical problems. However, boundary condition values can be uncertain if they come from measurements and/or they depend on certain parameters. In that case, the sensitivity to the boundary conditions needs to be analyzed. Currently, computational fluid dynamics simulations are often unfeasible for applications requiring testing of a large number of different system configurations, such as for sensitivity analysis. This has stimulated the development of modeling techniques that reduce the number of degrees of freedom of the high fidelity fluid flow models. Mathematical techniques are used to extract “features” of the high fidelity model and to replace the latter by a model with a lower number of degrees of freedom. In that way, the required computational time and computer memory usage is reduced. In this talk, I will present and discuss some reduced order modeling methods that I have developed for incompressible flows. In addition, I will focus on the challenge of imposing parametric boundary conditions at the reduced order level.

Matthias Schlottbom
We study the efficient numerical solution of linear inverse problems with operator valued data which arise, e.g., in seismic exploration, inverse scattering, or tomographic imaging. The high-dimensionality of the data space implies extremely high computational cost already for the evaluation of the forward operator, which makes a numerical solution of the inverse problem, e.g., by iterative regularization methods, practically infeasible. To overcome this obstacle, we develop a novel model reduction approach that takes advantage of the underlying tensor product structure of the problem and which allows to obtain low-dimensional certified reduced order models of quasi-optimal rank. A complete analysis of the proposed model reduction approach is given in a functional analytic setting and the efficient numerical construction of the reduced order models as well as of their application for the numerical solution of the inverse problem is discussed. In summary, the setup of a low-rank approximation can be achieved in an offline stage at essentially the same cost as a single evaluation of the forward operator, while the actual solution of the inverse problem in the online phase can be done with extremely high efficiency. The theoretical results are illustrated by application to a typical model problem in fluorescence optical tomography.


PhDays 2021
If COVID allows it, PhDays will be in the weekend of 29-30 May. More news will follow in the beginning of 2021.


The spring meeting is organized yearly by the Dutch-Flemish Scientific Computing Society (SCS), this year in cooperation with Technical University Delft.

Organizing comittee: Prof.dr.ir. Kees Vuik (TUD, ) Martine Anholt (CWI, Secretary SCS).


Support for this meeting has been obtained from Centrum Wiskunde & Informatica (CWI) and Technical University Delft. The Scientific Computing Society is very grateful for that support.