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

November 19, 2019

Article on UQ of discontinuous responses online at SIAM JSC

Our article on An Adaptive Minimum Spanning Tree Multielement Method for Uncertainty Quantification of Smooth and Discontinuous Responses is accepted at SIAM Journal on Scientific Computing and now available online.
Read More
October 30, 2019

Article on Bayesian Model Calibration online and open access

Our article on Bayesian Model Calibration with Interpolating Polynomials based on Adaptively Weighted Leja Nodes is now online from Communications in Computational Physics in open access form.
Read More
October 25, 2019

Postdoc vacancy

There is a postdoctoral vacancy on Bayesian calibration of wind turbine models. Update (Nov. 2019): the vacancy has been filled.
Read More
October 22, 2019 / conference

ACOS symposium

On Wednesday October 30, we will attend the ACOS symposium in Eindhoven.
Read More
October 22, 2019 / conference, presentation

Digital twin conference

On Thursday 31st October, Yous van Halder will speak at the Digital Twin conference in Eindhoven about real-time sloshing simulations.
Read More

Research team

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

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

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.
ArXiv e-print arXiv:1904.07021
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