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NMC 2021 (online series): International Day of Mathematics

  • 2021-03-15T15:00:00+01:00
  • 2021-03-15T17:30:00+01:00
March 15 Monday

Start: 2021-03-15 15:00:00+01:00 End: 2021-03-15 17:30:00+01:00

Everyone is welcome to attend, as part of the online series of NMC 2021 ((Dutch Mathematical Congress), the official celebration of the International Day of Mathematics.
Several captivating talks will be given by fellow mathematicians from different fields of research.
All Information and registration can be found on the website: https://mathematischcongres.nl/event/international-day-of-mathematics/

SC Seminar Benjamin Sanderse (CWI)

  • 2021-03-11T15:30:00+01:00
  • 2021-03-11T16:30:00+01:00
March 11 Thursday

Start: 2021-03-11 15:30:00+01:00 End: 2021-03-11 16:30:00+01:00

online

Seminar on Machine Learning and Uncertainty Quantification for Scientific Computing

Online https://cwi-nl.zoom.us/j/9201774084?pwd=OFBqM1dUenFreGdPUWEwZFYvMlJ6UT09

 

Multi-Level Neural Networks for PDEs with Uncertain Parameters

Benjamin Sanderse (CWI)

A novel multi-level method for partial differential equations with uncertain parameters is proposed. The principle behind the method is that the error between grid levels in multi-level 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 well-suited 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 state-of-the-art multi-level methods, especially in the case when complex PDEs (such as single-phase and free-surface flow problems) are concerned, or when high accuracy is required.

Workshop General Awareness Quantum Computing

  • 2021-02-26T13:00:00+01:00
  • 2021-02-26T16:30:00+01:00
February 26 Friday

Start: 2021-02-26 13:00:00+01:00 End: 2021-02-26 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)

  • 2021-02-25T16:00:00+01:00
  • 2021-02-25T17:00:00+01:00
February 25 Thursday

Start: 2021-02-25 16:00:00+01:00 End: 2021-02-25 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 positive-semidefiniteness (PSD) constraint and only enforce PSD-ness 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-`a-vis 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 k-PSD 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 k-PSD 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 k-PSD closure (ii) Observing a connection between matrices in the k-PSD 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 Zordo-Banliat

  • 2021-02-25T15:00:00+01:00
  • 2021-02-25T16:00:00+01:00
February 25 Thursday

Start: 2021-02-25 15:00:00+01:00 End: 2021-02-25 16:00:00+01:00

online

Maximilien de Zordo-Banliat (Safran Tech, Dynfluid Laboratory): Space-dependent Bayesian model averaging of turbulence models for compressor cascade flows.

Zoom link: https://cwi-nl.zoom.us/j/9201774084?pwd=OFBqM1dUenFreGdPUWEwZFYvMlJ6UT09

 

Space-dependent Bayesian model averaging of turbulence models for compressor cascade flows

 M. de Zordo-Banliat1,2, X. Merle2, G. Dergham1, P. Cinnella2

1Safran Tech, Modelling & Simulation, Rue des Jeunes Bois, Châteaufort, 78114 Magny-Les-Hameaux, France

2DynFluid 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 is-sue 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 space-dependent functions. Similarly, we propose a Space-Dependent Stacking (SDS) algorithm which modifies the stacking formalism to include space-dependent weights, based on a spatial decomposition method.

In this work, the above-mentioned methods are investigated in a Computational Fluid Dynamics(CFD) context. Specifically, CFD of engineering systems often relies on Reynolds-Averaged Navier-Stokes (RANS) models to describe the effect of (unresolved) turbulent motions onto the mean (re-solved) field. Since all turbulent motions are modelled, RANS turbulence models tend to be uncertain and case-dependent. 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 ex-pert judgment, that different RANS models tend to perform better in some flow regions and less in other regions, and consequently they should be assigned space-varying weights. For this reason, we implement and assess space-dependent 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 Space-Dependent Stacking (SDS) method based on Karhunen-Loeve 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 space-dependent weighted-average 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 constant-weight 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 Zordo-Banliat, X. Merle, G. Dergham, and P. Cinnella. Bayesian model-scenario averaged predictions of compressor cascade flows under uncertain turbulence models. Computers & Fluids, 201:104473, 2020.

SEN SYMPOSIUM

  • 2021-02-18T00:00:00+01:00
  • 2021-02-19T23:59:59+01:00
February 18 Thursday

Start: 2021-02-18 00:00:00+01:00 End: 2021-02-19 23:59:59+01:00

Online

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:

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.

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