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

September 21, 2020

Torque 2020 conference and publications

Next week is the Science of Making Torque conference (virtual) that was supposed to happen earlier this year in Delft. We have two open-acces papers that are published as part...
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August 5, 2020

New publication in Journal of Computational Physics

My article on Energy-Conserving Reduced-Order Models is now online at the Journal of Computational Physics.
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July 16, 2020

Article in ERCIM News, Solving Engineering Problems with Machine Learning

A new ERCIM News edition was released in July 2020, including a contribution of Yous van Halder and myself on our work on using neural networks to accelerate sloshing simulations....
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May 13, 2020

Paper on efficient Bayesian prediction accepted and online at JCP

This is exciting work where we show how we can circumvent Markov-chain Monte Carlo in order to do Bayesian inference. Please visit the Journal of Computational Physics website.
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April 29, 2020

New preprint online: Multi-level neural networks for PDEs with uncertain parameters

Please visit ArXiv to download the preprint.
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Laurent van den Bos

Laurent van den Bos

PhD candidate

Yous van Halder

Yous van Halder

PhD candidate

Jurriaan Buist

Jurriaan Buist

PhD candidate

Prashant Kumar

Prashant Kumar

Postdoc

Vinit Dighe

Vinit Dighe

Postdoc

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