Towards truly predictive computational science

The future of computational science lies in the combination of physical modelling and data-driven techniques. In my research team at CWI, part of 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

March 30, 2021

Uncertainty quantification of exit strategies in the COVID pandemic

Together with Erasmus Medical Centre, we have performed an analysis of the effect of uncertainties (in parameters related to exit strategies) on the number of people expected on the intensive...
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January 28, 2021

New article in Journal of Computational and Applied Mathematics

Constructing positive quadrature rules is an important ongoing challenge in fields such as uncertainty quantification. Our new article gives fundamental insights in the geometric interpretation of such rules.
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Katerina Papadaki

Katerina Papadaki

Intern

Syver Agdestein

Syver Agdestein

PhD candidate

Yous van Halder

Yous van Halder

PhD candidate

Jurriaan Buist

Jurriaan Buist

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