Uncertainty Quantification - Semester programme

Wouter Edeling from my group will organise a Semester Programme on Uncertainty Quantification for High-Dimensional Problems, which will take place in Fall 2024 at Centrum Wiskunde & Informatica (Amsterdam). This is a great opportunity to connect with the international community and learn more about recent developments in the field. The program will feature an autumn school (7-11 October), a workshop (11-15 November) and hybrid seminars.
The organizers are Peter Coveney (UvA/University College London), Richard Dwight (TU Delft), Wouter Edeling (CWI), Olga Mula (TU Eindhoven), Laura Scarabosio (Radboud University).

Scientific Machine Learning - Semester Programme

My group has been organising a very successful CWI Semester Programme on Scientific Machine Learning.  You can find most of the material here. Here’s a quick overview of our main events:

Autumn School, October 9-13

Introducing PhD students, postdocs and other early career researchers to SciML, with lectures and hands-on tutorials on topics like reduced-order models, neural ODEs, and data-driven multi-scale methods. World experts like Steve Brunton and Hod Lipson are giving their lectures in Amsterdam.

Industry & Society, November 23

Bridging theory and practice, with SciML from a practical and industrial point of view, with speakers from KNMI and Deltares, amongst others. (More information here.)

Workshop, December 6-8

Connecting Dutch researchers who are interested in SciML to the international community. We have a stellar line-up with speakers like Jan Hesthaven, Sid Mishra and Dirk Hartmann. (Click here for details.)

Seminar++: throughout the program

In October, November and December, renowned international researchers will visit CWI for several days in order to present, discuss and interact on open problems in SciML with the Dutch community. The seminars can be followed online as well. (More information here)

Poster

Towards truly predictive computational science

The future of computational science lies in the combination of physical modelling and data-driven techniques. In my research group at CWI, the Scientific Computing group, we develop new methods and algorithms that enable the transition to truly predictive models. For this purpose, we work at the intersection of different disciplines: uncertainty quantification, partial differential equations and discretization methods, computational fluid dynamics, machine learning, reduced-order modeling, and Bayesian inference.

The models that we develop are applied to a variety of (industrial) applications: for example, sloshing of liquid natural gas in tanker ships, aeroelastic predictions for wind turbines, and transport of multiphase flow in pipelines. Our work also forms an enabler for recent techniques such as Digital Twins.

Sloshing of liquid simulated with smoothed particle hydrodynamics. Courtesy of Yous van Halder.

News

November 16, 2021

Three new colleagues in Scientific Computing

Very happy to announce that Henrik Rosenberger, Hugo Melchers, and Robin Klein have joined our team to work on their PhD/MSc theses, on topics related to closure modeling, reduced order...
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June 28, 2021

Calibrating aerodynamic wind turbine models: preprint in Wind Energy Science Discussions

Predictions of aerodynamic wind turbine models are full of uncertainties. We (CWI, TNO) are proposing a structured way to analyse and quantify those with our latest article. Now available as...
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May 3, 2021

Syver Agdestein joins as PhD candidate

Starting May 1st, Syver Døving Agdestein joins the Scientific Computing group and will work on the Vidi grant 'discretize first, reduce next', in which new approaches to closure modeling for...
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April 19, 2021

New preprint on structure-preserving discretization of two-phase flow

We've written an exciting article in which a new approach is proposed to tackle the ill-posedness of the two-fluid model. We proof energy conservation of the model as a way...
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Jurriaan Buist

Jurriaan Buist

PhD candidate

Toby van Gastelen

Toby van Gastelen

PhD candidate

Robin Klein

Robin Klein

PhD candidate

Henrik Rosenberger

Henrik Rosenberger

PhD candidate

Syver Agdestein

Syver Agdestein

PhD candidate

Pardeep Kumar

Pardeep Kumar

PhD candidate

Recent research highlights

Discretize first, filter next: model-data consistent closure models for large-eddy simulation

We propose a new neural network based large eddy simulation framework for the incompressible Navier-Stokes equations based on the paradigm […]

Energy-conserving neural networks for turbulence closure modeling

In turbulence modeling, and more particularly in the Large-Eddy Simulation (LES) framework, we are concerned with finding closure models that […]

Stable reduced-order models for fluid flows

Reduced-order models are used widely to make fluid flow simulation computationally tractable for example for the purpose of real-time control [...]

Quantifying uncertainty in fatigue loads on offshore wind turbines

When designing wind turbines, it is necessary to give proof that the turbine will be able to withstand the environmental […]

Recent publications

Non-linearly stable reduced-order models for incompressible flow with energy-conserving finite volume methods

ArticleJournal paper
Sanderse, B.
Journal of Computational Physics, Volume 421, 15 November 2020
Publication year: 2020

PDE/PDF-informed adaptive sampling for efficient non-intrusive surrogate modelling

ArticlePreprint
van Halder, Yous and Sanderse, Benjamin and Koren, Barry
ArXiv e-print arXiv:1907.04022
Publication year: 2019

Machine learning for closure models in multiphase flow applications

ArticleConference proceedings
Buist, Jurriaan and Sanderse, Benjamin and van Halder, Yous
In UNCECOMP 2019, 3rd ECCOMAS Thematic Conference on Uncertainty Quantification in Computational Sciences and Engineering, M. Papadrakakis, V. Papadopoulos, G. Stefanou (eds.) Crete, Greece, 24-26 June 2019, 2019
Publication year: 2019

Fatigue design load calculations of the offshore NREL 5MW benchmark turbine using quadrature rule techniques

ArticlePreprint
van den Bos, L. M. M. and Bierbooms, W. A. A. M. and Alexandre, A. and Sanderse, B. and van Bussel, G. J. W.
Wind Energy, 2020
Publication year: 2019

Adaptive sampling-based quadrature rules for efficient Bayesian prediction

ArticlePreprint
van den Bos, L. M. M. and Sanderse, B. and Bierbooms, W. A. A. M.
ArXiv e-print arXiv:1907.08418
Publication year: 2019

Generating nested quadrature rules with positive weights based on arbitrary sample sets

ArticlePreprint
van den Bos, L. M. M. and Sanderse, B. and Bierbooms, W. A. A. M. and van Bussel, G. J. W.
SIAM/ASA Journal on Uncertainty Quantification, Vol. 8, No. 1, pp. 139-169
Publication year: 2020