-<!doctype html> <html lang=en > <meta charset=UTF-8 > <meta name=viewport content="width=device-width, initial-scale=1"> <link rel=stylesheet href="/css/franklin.css"> <link rel=stylesheet href="/css/basic.css"> <link rel=icon href="/assets/favicon.png"> <title>Scientific Computing Group CWI</title> <header> <nav> <ul> <li><a href="/">Home</a> <li><a href="/publications/">Publications</a> <li><a href="/software/">Software</a> </ul> <img src="/assets/hamburger.svg" id=menu-icon > </nav> </header> <div class=franklin-content ><h1 id=the_scientific_computing_group_at_cwi ><a href="#the_scientific_computing_group_at_cwi" class=header-anchor >The Scientific Computing Group at CWI</a></h1> <p>The Scientific Computing group at CWI develops efficient mathematical methods to simulate and predict real-world phenomena with inherent uncertainties. Our two main research themes are <strong>scientific machine learning</strong> and <strong>uncertainty quantification</strong>, and the topics within these themes are neural ODEs, closure models for turbulence, reduced-order models, discretization techniques, stochastic parameterizations, generative models, data assimilation.</p> <p>Our official CWI page with general information can be found <a href="https://www.cwi.nl/en/groups/scientific-computing/">here</a>. On the current page, you can find more detailed information such as the software that we develop in the group and other useful information such as the material used in the <a href="https://github.com/ScientificComputingCWI">semester programmes</a> that we organize, in particular the <a href="https://github.com/ScientificComputingCWI/SemesterProgramme-SciML">SciML</a> and <a href="https://github.com/ScientificComputingCWI/SemesterProgramme-UQ">UQ</a> semester programmes. Note the upcoming semester programme 2025 on <a href="https://www.cwi.nl/en/education/semester-programmes/cwi-research-semester-programs/synergies-in-numerical-linear-algebra-and-machine-learning/">Synergies in numerical linear algebra and machine learnin</a>!</p> <p>Please check our <a href="https://github.com/ScientificComputingCWI">GitHub page</a> with group repositories and our <a href="/software/">software page</a>.</p> <h2 id=members ><a href="#members" class=header-anchor >Members</a></h2> <p>Current members: Benjamin Sanderse (group leader), Wouter Edeling (staff), Daan Crommelin (staff), Syver Agdestein (PhD candidate), Toby van Gastelen (PhD candidate), Rik Hoekstra (PhD candidate), Pardeep Kumar (PhD candidate), Marius Kurz (postdoc), Nikolaj Mücke (postdoc), Henrik Rosenberger (PhD candidate), Robin Klein (PhD candidate), Barry Koren (advisor). <strong>News</strong>: Dimitris Loukrezis will join our group as a tenure track researcher, starting March 1st, 2025. Welcome Dimitris!</p> <p><img src="pictures/IMG_4468%20copy.jpg" alt="SC group at ECCOMAS 2024" /></p> <h2 id=group_seminar ><a href="#group_seminar" class=header-anchor >Group seminar</a></h2> <p>To receive news and Zoom links for our group seminar, you can contact Wouter Edeling at <code>wouter.edeling@cwi.nl</code>. For more information, see our <a href="https://www.cwi.nl/en/groups/scientific-computing/uq-seminar/seminar-ml-uq-sc/">seminar page</a>.</p> <ul class=blog-posts ><li><span><i>2024-11-19</i></span><b><a href="/seminars/20241119/">Seminar Jelmer Wolterink</a></b><li><i class=description >Exploiting Symmetries for Personalized Hemodynamics Modeling in Cardiovascular Disease</i><li><span><i>2024-09-12</i></span><b><a href="/seminars/20240911/">Seminar Dimitris Loukrezis</a></b><li><i class=description >Scientific Machine Learning and Uncertainty Quantification for Predictive Digital Twins</i><li><span><i>2024-09-06</i></span><b><a href="/seminars/20240906/">Seminar Michael Abdelmalik</a></b><li><i class=description >Neural Green's Operators for Parametric Partial Differential Equations</i><li><span><i>2024-06-20</i></span><b><a href="/seminars/20240620/">Seminar Francesca Bartolucci</a></b><li><i class=description >Representation Equivalent Neural Operators: A Framework for Alias-free Operator Learning</i><li><span><i>2024-05-27</i></span><b><a href="/seminars/20240527/">Seminar Beatriz Moya</a></b><li><i class=description >Exploring the role of geometric and learning biases in Model Order Reduction and Data-Driven simulation</i></ul> <h2 id=news ><a href="#news" class=header-anchor >News</a></h2> <ul class=blog-posts ><li><span><i>2024-07-26</i></span><b><a href="/news/AINed_XS/">AINed XS funding</a></b><li><i class=description >AINed XS funding for generative AI</i><li><span><i>2024-06-19</i></span><b><a href="/news/semesterprogramme2024/">Semester programme 2024</a></b><li><i class=description >Semester programme on uncertainty quantification 2024</i></ul> <h2 id=publications ><a href="#publications" class=header-anchor >Publications</a></h2> <p><a href="https://ir.cwi.nl/#facet=affiliation_label_partOf:Scientific%20Computing">Full list of publications at CWI's institutional repository</a></p> <ul class=blog-posts ><li><span><i>2025-07-22</i></span><b><a href="/publications/agdesteinExactClosureDiscrete2025/">Exact closure for discrete large-eddy simulation</a></b><li><i class=description >Syver Døving Agdestein, Roel Verstappen, Benjamin Sanderse, arXiv preprint arXiv:2507.17051</i><li><span><i>2025-03-01</i></span><b><a href="/publications/keith2025/">Scientific Machine Learning: A Symbiosis</a></b><li><i class=description >Keith, O'Leary-Roseberry, Sanderse, Scheichl, van Bloemen Waanders, Scientific Machine Learning: A Symbiosis</i><li><span><i>2025-03-01</i></span><b><a href="/publications/sanderse2024/">Scientific machine learning for closure models in multiscale problems: A review</a></b><li><i class=description >Sanderse, Stinis, Maulik, Ahmed, Scientific machine learning for closure models in multiscale problems: A review</i><li><span><i>2025-02-01</i></span><b><a href="/publications/agdesteinDiscretizeFirstFilter2025/">Discretize first, filter next: learning divergence-consistent closure models for large-eddy simulation</a></b><li><i class=description >Syver Døving Agdestein, Benjamin Sanderse, Journal of Computational Physics, 2025</i><li><span><i>2024-12-15</i></span><b><a href="/publications/rik/">Reduced Data-Driven Turbulence Closure for Capturing Long-Term Statistics</a></b><li><i class=description >Rik Hoekstra, Daan Crommelin, Wouter Edeling, in Computers and Fluids</i><li><span><i>2024-07-01</i></span><b><a href="/publications/vangastelen2024/">Energy-Conserving Neural Network for Turbulence Closure Modeling</a></b><li><i class=description >Toby van Gastelen, Wouter Edeling, Benjamin Sanderse, in Journal of Computational Physics</i><li><span><i>2022-11-24</i></span><b><a href="/publications/agdestein2022/">Discretize first, filter next – a new closure model approach</a></b><li><i class=description >Syver Døving Agdestein, Benjamin Sanderse, in Eccomas 2022</i></ul> <div class=page-foot > <a href="http://creativecommons.org/licenses/by-sa/4.0/">CC BY-SA 4.0</a> Scientitic Computing Group CWI. Last modified: August 28, 2025. Website built with <a href="https://github.com/tlienart/Franklin.jl">Franklin.jl</a> and the <a href="https://julialang.org">Julia programming language</a>. </div> </div>
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