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SC Seminar Benjamin Sanderse (CWI)
 20210311T15:30:00+01:00
 20210311T16:30:00+01:00
SC Seminar Benjamin Sanderse (CWI)
Start: 20210311 15:30:00+01:00 End: 20210311 16:30:00+01:00
Seminar on Machine Learning and Uncertainty Quantification for Scientific Computing
Online https://cwinl.zoom.us/j/9201774084?pwd=OFBqM1dUenFreGdPUWEwZFYvMlJ6UT09
MultiLevel Neural Networks for PDEs with Uncertain Parameters
Benjamin Sanderse (CWI)
A novel multilevel method for partial differential equations with uncertain parameters is proposed. The principle behind the method is that the error between grid levels in multilevel methods has a spatial structure that is by good approximation independent of the actual grid level. Our method learns this structure by employing a sequence of convolutional neural networks, that are wellsuited to automatically detect local error features as latent quantities of the solution. Furthermore, by using the concept of transfer learning, the information of coarse grid levels is reused on fine grid levels in order to minimize the required number of samples on fine levels. The method outperforms stateoftheart multilevel methods, especially in the case when complex PDEs (such as singlephase and freesurface flow problems) are concerned, or when high accuracy is required.
Workshop General Awareness Quantum Computing
 20210226T13:00:00+01:00
 20210226T16:30:00+01:00
Workshop General Awareness Quantum Computing
Start: 20210226 13:00:00+01:00 End: 20210226 16:30:00+01:00
The workshop General Awareness Quantum Computing introduces you to the general principles of quantum computing and how such a computer can be used. The workshop aimed at an audience which does not have prior knowledge of quantum physics, but which does possess common knowledge of everyday computers. When completed, you will have a general understanding of the possibilities and what to expect of quantum computers in the future. This workshop gives you a clear focus on business relevance: no difficult physics, but a clear focus on disruptive opportunities and threats.
During the 3.5 hours the workshop takes, first some elementary concepts of quantum mechanics are explained. It becomes clear that a quantum computer has a tremendous potential to solve various problems, but there are formidable challenges in building the right soft and hardware. Moreover, there exist many myths and suggestive promises about the possibilities of quantum computers, many of which turn out to be false. Together with other participants and the presenters, you will discuss how quantum computing influences your business and what you should do to create opportunities. One of Quantum.Amsterdam’s core values is to avoid hyping quantum technology, but rather to tell a balanced and honest story. We are grateful for collaboration with ING, Rabobank and ABN AMRO during the development of this workshop.
The workshop is given online using Cisco Webex or MS Teams. English language is used.
The next workshops will be provided on:
26 February 2021
26 March, 2021
23 April 2021
Dutch Seminar on Optimization with Santanu Dey from Georgia Tech (Online series)
 20210225T16:00:00+01:00
 20210225T17:00:00+01:00
Dutch Seminar on Optimization with Santanu Dey from Georgia Tech (Online series)
Start: 20210225 16:00:00+01:00 End: 20210225 17:00:00+01:00
Everyone is welcome to attend the online lecture of Santanu Dey with the title ' Sparse PSD approximation of the PSD cone'.
Abstract: While semidefinite programming (SDP) problems are polynomially solvable in theory, it is often difficult to solve large SDP instances in practice. One computational technique used to address this issue is to relax the global positivesemidefiniteness (PSD) constraint and only enforce PSDness on smaller k × k principal submatrices — we call this the sparse SDP relaxation. Surprisingly, it has been observed empirically that in some cases this approach appears to produce bounds that are close to the optimal objective function value of the original SDP. In this talk, we formally attempt to compare the strength of the sparse SDP relaxation vis`avis the original SDP from a theoretical perspective. In order to simplify the question, we arrive at a data independent version of it, where we compare the sizes of SDP cone and the kPSD closure, which is the cone of matrices where PSDness is enforced on all k × k principal submatrices. In particular, we investigate the question of how far a matrix of unit Frobenius norm in the kPSD closure can be from the SDP cone. We provide two incomparable upper bounds on this farthest distance as a function of k and n. We also provide matching lower bounds, which show that the upper bounds are tight within a constant in different regimes of k and n. Other than linear algebra techniques, we use probabilistic methods to arrive at these bounds. Two other key ingredients are: (i) observing that the hyperbolicity cone of the elementary symmetric polynomial is a convex relaxation of the set of eigenvalues of matrices in kPSD closure (ii) Observing a connection between matrices in the kPSD closure and matrices satisfying the restricted isometry property (RIP).
This is joint work with Grigoriy Blekherman, Marco Molinaro, Kevin Shu, and Shengding Sun.
More information, also about (free) registration, can be found on the website.
SC Seminar Maximilien de ZordoBanliat
 20210225T15:00:00+01:00
 20210225T16:00:00+01:00
SC Seminar Maximilien de ZordoBanliat
Start: 20210225 15:00:00+01:00 End: 20210225 16:00:00+01:00
Maximilien de ZordoBanliat (Safran Tech, Dynfluid Laboratory): Spacedependent Bayesian model averaging of turbulence models for compressor cascade flows.
Zoom link: https://cwinl.zoom.us/j/9201774084?pwd=OFBqM1dUenFreGdPUWEwZFYvMlJ6UT09
Spacedependent Bayesian model averaging of turbulence models for compressor cascade flows
M. de ZordoBanliat^{1,2}, X. Merle^{2}, G. Dergham^{1}, P. Cinnella^{2}
^{1}Safran Tech, Modelling & Simulation, Rue des Jeunes Bois, Châteaufort, 78114 MagnyLesHameaux, France
^{2}DynFluid Laboratory  Arts et Métiers ParisTech  151 boulevard de l’Hôpital, 75013 Paris, France
Abstract
Predictions of systems described by multiple alternative models is of importance for many applications in science and engineering, namely when it is not possible to identify a model that significantly outperforms every other model for each criterion. Two mathematical approaches tackling this issue are Bayesian Model Averaging (BMA) [1, 2], which builds an average of the concurrent models weighted by their marginal posteriors probabilities, and Stacking [3, 4], where the unknown prediction is projected on a basis of alternative models, with weights to be learned from data. In both approaches, the weights are generally constant throughout the domain. More recently, Yu et al. [5] have proposed the Clustered Bayesian Averaging (CBA) algorithm, which leverages an ensemble of Regression Trees (RT) to infer weights as spacedependent functions. Similarly, we propose a SpaceDependent Stacking (SDS) algorithm which modifies the stacking formalism to include spacedependent weights, based on a spatial decomposition method.
In this work, the abovementioned methods are investigated in a Computational Fluid Dynamics(CFD) context. Specifically, CFD of engineering systems often relies on ReynoldsAveraged NavierStokes (RANS) models to describe the effect of (unresolved) turbulent motions onto the mean (resolved) field. Since all turbulent motions are modelled, RANS turbulence models tend to be uncertain and casedependent. Quantifying and reducing such uncertainties is then of the utmost importance for aerodynamics design in general, and specifically for the analysis and optimization of complex turbo machinery flows. In previous work [6], the present authors used Bayesian model averages of RANS models for providing improved predictions of a compressor cascade configuration, alongside with a quantification of confidence intervals associated with modelling uncertainties. Constant weights throughout the field were used. It is however expected, based on theoretical considerations and expert judgment, that different RANS models tend to perform better in some flow regions and less in other regions, and consequently they should be assigned spacevarying weights. For this reason, we implement and assess spacedependent averages of RANS models for compressor flow predictions. More precisely the focus is put on two alternative algorithms: (i) a version of CBA adapted to flow variable fields, and (ii) a SpaceDependent Stacking (SDS) method based on KarhunenLoeve decomposition of the mixture weights. Flow regions are described using selected features, formulated as functions of the mean flow quantities. Given a set of concurrent RANS models and a database of reference flow data corresponding to various operating conditions, the two algorithms are first trained against data, and subsequently applied to the prediction of an unobserved flow, i.e. another operating condition of the compressor cascade. The algorithms assign a probability to each model in each region of the feature space, based on their ability to accurately predict selected Quantities of Interest (QoI) in this region. The spacedependent weightedaverage of the RANS models applied to the prediction scenario is used to reconstruct the expected solution and the associated confidence intervals. Preliminary results show that both of the methods generally yield more accurate solutions than the constantweight BMA method, and provide a valuable estimate of the uncertainty intervals.
References
[1] David Madigan, Adrian E Raftery, C Volinsky, and J Hoeting. Bayesian model averaging. In Proceedings of the AAAI Workshop on Integrating Multiple Learned Models, Portland, OR, pages 77–83,1996.
[2] Jennifer A. Hoeting, David Madigan, Adrian E. Raftery, and Chris T. Volinsky. Correction to: “bayesian model averaging: a tutorial” [statist. sci. 14 (1999), no. 4, 382–417; mr 2001a:62033]. Statist. Sci., 15(3):193–195, 08 2000.
[3] David H. Wolpert. Stacked generalization. Neural networks, 5(2):241–259, 1992.
[4] Leo Breiman. Stacked regressions. Machine learning, 24(1):49–64, 1996.
[5] Qingzhao Yu, Steven N. MacEachern, and Mario Peruggia. Clustered bayesian model averaging. Bayesian Anal., 8(4):883–908, 12 2013.
[6] M. de ZordoBanliat, X. Merle, G. Dergham, and P. Cinnella. Bayesian modelscenario averaged predictions of compressor cascade flows under uncertain turbulence models. Computers & Fluids, 201:104473, 2020.
SEN SYMPOSIUM
 20210218T00:00:00+01:00
 20210219T23:59:59+01:00
SEN SYMPOSIUM
Start: 20210218 00:00:00+01:00 End: 20210219 23:59:59+01:00
The seventh Dutch national symposium on software engineering (SEN) will be held in the afternoons of Thursday 18th and Friday 19th February 2021. At this conference we bring together the Dutch software engineering community.
The symposium is organized by VERSEN, the Dutch National Association for Software Engineering. The program will feature keynotes, invited presentations, and contributed “lightning talks”. We will offer talks by the following invited speakers:
 Barbora Buhnova, Masaryk University Brno, Czech Republic
 Yanja Dajsuren, Eindhoven University of Technology, the Netherlands
 Emitza Guzman, Vrije Universiteit Amsterdam, the Netherlands
 JoostPieter Katoen, RWTH Aachen University, Germany
 Dennis Komm, PH Graubünden, ETH Zürich, Switzerland
 Burcu Kulahcioglu Ozkan, Delft University of Technology, the Netherlands
 Christian Prehofer, DENSO Automotive, Germany
 Joost Visser, Leiden University, the Netherlands
Organising committee
 Helle Hvid Hansen, University of Groningen
 Ilias Gerostathopoulos, Vrije Universiteit Amsterdam
 Nils Jansen, Radboud University Nijmegen
 Jurgen Vinju, Centrum Wiskunde & Informatica, TU Eindhoven (local organisation)
 Tijs van der Storm, Centrum Wiskunde & Informatica, University of Groningen (local organisation)
Participation is free of charge, but please register here.
Kickoff of the Cultural AI LaB
 20210217T16:00:00+01:00
 20210217T17:00:00+01:00
Kickoff of the Cultural AI LaB
Start: 20210217 16:00:00+01:00 End: 20210217 17:00:00+01:00
Kickoff of the Cultural AI Lab
17022021 from 16:00 to 17:00
How can we design Artificial Intelligence that is aware of the subtle and subjective complexity of human culture? This webinar marks the starting point of the new ICAI Lab on Cultural AI, in which CWI, KNAW Humanities Cluster, KB National Library of the Netherlands, Dutch Institute of Sound and Vision, Rijksmuseum, TNO, University of Amsterdam, and Vrije Universiteit Amsterdam join forces to develop AItools applicable in the cultural heritage sector.
Members
Associated Members
Publications

Vinju, J.J. (2020). Noisy numbers : column I/O magazine. I/O Magazine : ICT Research Platform Nederland, 17(3), 3–3.

van Binsbergen, L.T, Verano Merino, M, Jeanjean, P, van der Storm, T, Combemale, B, & Barais, O. (2020). A principled approach to REPL interpreters. In Onward! 2020  Proceedings of the 2020 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software, Colocated with SPLASH 2020 (pp. 84–100). doi:10.1145/3426428.3426917

Verano Merino, M, & van der Storm, T. (2020). Blockbased syntax from contextfree grammars. In SLE 2020  Proceedings of the 13th ACM SIGPLAN International Conference on Software Language Engineering, Colocated with SPLASH 2020 (pp. 283–295). doi:10.1145/3426425.3426948

van der Storm, T, & Bakker, G. (2020). MATLAB doesn't love me: An essay. In ACM International Conference Proceeding Series (pp. 97–101). doi:10.1145/3397537.3397557

van Rozen, R.A. (2020, February 19). Languages of games and play : automating game design and enabling live programming. IPA dissertation series.

Aarssen, R.T.A, & van der Storm, T. (2020). Highfidelity metaprogramming with separator syntax trees. In Proceedings of the ACM SIGPLAN Workshop on Partial Evaluation and Program Manipulation (pp. 27–37). doi:10.1145/3372884.3373162

Azadbakht, K, de Boer, F.S, Bezirgiannis, N, & de Vink, E.P. (2019). A formal actorbased model for streaming the future. Science of Computer Programming, 186. doi:10.1016/j.scico.2019.102341

Soethout, T.M, van der Storm, T, & Vinju, J.J. (2019). Static local coordination avoidance for distributed objects. In AGERE 2019  Proceedings of the 9th ACM SIGPLAN International Workshop on Programming Based on Actors, Agents, and Decentralized Control, colocated with SPLASH 2019 (pp. 21–30). doi:10.1145/3358499.3361222

Schaefer, I, Reichenbach, C, & van der Storm, T (Eds.). (2019). GPCE 2019: Proceedings of the 18th ACM SIGPLAN International Conference on Generative Programming: Concepts and Experiences. In I Schaefer, C Reichenbach, & T van der Storm (Eds.), .

Afroozeh, A, & Izmaylova, A. (2019, June 11). Practical general topdown parsers. IPA dissertation series.
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