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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Jurriaan Buist

Jurriaan Buist

PhD candidate

Toby van Gastelen

Toby van Gastelen

PhD candidate

Robin Klein

MSc thesis student

Hugo Melchers

MSc thesis student

Henrik Rosenberger

Henrik Rosenberger

PhD candidate

Syver Agdestein

Syver Agdestein

PhD candidate

Recent research highlights

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 […]

Uncertainty in sloshing with new adaptive sampling techniques

Sloshing of liquefied natural gas (LNG) in large transport tankers is an important issue that limits the operational envelope of […]

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