2023 Springmeeting

Participants of the 2022 Spring meeting at KU Leuven

Wednesday May 31, 2023, the Dutch-Flemish Scientific Computing Society organizes its annual spring meeting. This year it takes place at Technical University Eindhoven. A mix of young and senior researchers are invited to present their research.

Participation including lunch is free of charge but you will need to register. Registration will open in March.

Location:
TU Eindhoven
Zwarte Doos, gebouw 4 it is a 10 minute walk from the NS station Eindhoven Centraal

Organization

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

Organizing comittee: Barry Koren (TU Eindhoven) and Martine Anholt (CWI, Secretary SCS).

Support for this meeting has been obtained from Centrum Wiskunde & Informatica (CWI) and TU Eindhoven.

Draft Program 2023

09:00-09:30	Registration, coffee and tea
09:30-10:10	Svetlana Dubinkina, VU Amsterdam
10:10-10:35	Emil Løvbak, KU Leuven
10:35-11:00	Fang Fang, Technical University Delft
11:00-11:30	Coffee and tea
11:30-11:55	Philipp Horn,Technical University Eindhoven
11:55-12:20	Jonas Thies, Technical University Delft
12:20-12:30	Group picture
12:30-13:30	Lunch
13:30-13:55	Mariya Ishteva, KU Leuven
13:55-14:20	Pascal den Boef, Technical University Eindhoven
14:20-14:50	Coffee, tea and refreshments
14:50-15:15	Anne Eggels, Sioux Technologies
15:15-15:55	Wim Vanroose, University of Antwerp
15:55-16:00	Closure

Speakers Spring meeting SCS 2023

	Svetlana Dubinkina, VU Amsterdam
	Wim Vanroose, University of Antwerp

	Anne Eggels, Sioux Technologies
	Fang Fang, Technical University Delft Dr. Fang Fang obtained a PhD in Computational Finance from TU Delft in 2010, based on the innovation of “the COS method”. Since 2021 she has been working for TU Delft as a part-time assistant professor. She is also a senior quant consultant and a modelling expert, with 14 years hands-on experience in pricing model validation and risk model development at Tier-1 financial institutions in the Netherlands. Her research interest lies in improving numerical methods and models for 1) risk quantification and allocation, 2) derivative pricing and 3) time series predictions. Courses she teaches/moderates include Computational Finance (Msc), Advanced Credit Risk Management (MOOC course joint prepared by TU Delft and Deliotte) and Introduction of Credit Risk Management (MOOC by TU Delft).
	Mariya Ishteva, KU Leuven
	Jonas Thies, Technical University Delft

	Pascal den Boef, Technical University Eindhoven
	Philipp Horn, Technical University Eindhoven Philipp Horn is a PhD student in the UNRAVEL project at TU Eindhoven. The current focus of his research are structure preserving neural networks for Hamiltonian systems. He obtained his B.Sc. degree in Simulation Technology from the University of Stuttgart. Followed by a double master program in Simulation Technology at the University of Stuttgart and Industrial and Applied Mathematics at TU Eindhoven. After his studies he shortly had a position as Junior Researcher at DIFFER in Eindhoven, researching structure preserving neural network surrogate models for fusion simulation
	Emil Løvbak, KU Leuven Emil obtained a BSc in Computer Science and Electrical engineering and an MSc in Mathematical Engineering from KU Leuven. After four years as a PhD Fellow of the Research Foundation Flanders, he is currently a researcher in the NUMA group at KU Leuven. His research areas cover multilevel Monte Carlo methods, stochastic optimization and kinetic equations.

Abstracts Spring meeting SCS 2023

Svetlana Dubinkina, VU Amsterdam
Wim Vanroose, University of Antwerp
Anne Eggels, Sioux Technologies
Fang Fang, Technical University Delft	A Novel Fourier-cosine method for risk quantification and allocation of credit portfolios Credit risk quantification and allocation in the factor-copula model framework underlies various practical applications in the banking industry. The popular numerical method in the banking industry is Monte Carlo (MC) simulation, which not only takes a considerable amount of computational time for large portfolios, but also fails to return reliable results when it comes to risk allocation at a standard high quantile like 99.9%. Herewith we present a novel Fourier-cosine method, which not only serves as a fast solver for portfolio-level risk quantification, but also fills the niche in literature that an accurate numerical method for risk allocation is lacking. The key insight is that, compared to directly estimating the portfolio loss distribution, it can be much more efficient to solve the characteristic function (ch.f.) instead, after which the ch.f. can be inverted to recover the cumulative distribution function (CDF) semi-analytically via the popular Fourier-cosine (COS) method in the field of option pricing but with some extension. We therefore name this method the COS method. As for allocation of risk measures, we show that, via the Bayes law, the original problem can be transformed to the evaluation of a conditional CDF, which can again be solved following the same insight. Theoretical proof of the error convergence is also provided, which effectively justifies the stability and accuracy of this method in recovering CDFs of discrete random variables in general. For real-sized portfolios, the calculation speed and accuracy are tested to be significantly superior to Monte Carlo simulation in the two-factor set-up. A Gaussian copula and a Gaussian-t hybrid copula are taken as examples to illustrate the flexibility of this method regarding copula choices; Value-at-Risk, Expected Shortfall (ES) and Euler allocation of ES are risk metrics selected for testing. The potential application scope is wide: Economic Capital for Banking Book, Default Risk Charge for Trading Book, valuation of credit derivatives, etc.
Mariya Ishteva, KU Leuven
Jonas Thies, Technical University Delft
Pascal den Boef, Technical University Eindhoven
Philipp Horn, Technical University Eindhoven	Structure-Preserving Neural Networks for Hamiltonian Systems When solving Hamiltonian systems using numerical integrators, preserving the symplectic structure is crucial. We analyze whether the same is true if neural networks (NN) are used. In order to include the symplectic structure in the NN's topology we formulate a generalized framework for two well-known NN topologies and discover a novel topology outperforming all others. We find that symplectic NNs generalize better and give more accurate long-term predictions than physics-unaware NNs.
Emil Løvbak, KU Leuven	Adjoint Monte Carlo particle methods with reversible random number generators When solving optimization problems constrained by high-dimensional PDEs, Monte Carlo particle methods are often the only practical approach to simulate the PDE. Unfortunately, these methods introduce noise in the computed particle distributions and, as a consequence, evaluations of the objective function. Through an adjoint-based approach, we can compute the corresponding gradient down to machine precision through a similar Monte Carlo simulation. However, this approach requires retracing the particle trajectories from the constraint simulation, backwards in time, when computing the gradient. When storing these paths for large-scale simulations, one quickly runs into memory issues. In this talk, we solve these memory issues by regenerating particle trajectories backward in time. To do so, we reverse the pseudorandom number generator used to generate the paths in the constraint simulation. After describing our reversible approach, we demonstrate o wit outperforms prior approaches on some concrete test-problems.