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Korteweg-de Vries Institute for Mathematics Korteweg-de Vries Institute for Mathematics

Prof. dr. D.T. (Daan) Crommelin

Faculty of Science
KDV
Photographer: Dirk Gillissen
Visiting address
  • Science Park 107
  • Room number: F3.15
Postal address
  • Postbus 94248
    1090 GE Amsterdam
Contact details
  • Publications

    2024

    • del Razo, M. J., Crommelin, D., & Bolhuis, P. G. (2024). Data-driven dynamical coarse-graining for condensed matter systems. Journal of Chemical Physics, 160(2), Article 024108. https://doi.org/10.1063/5.0177553 [details]
      • Downloads

    2023

    2022

    2021

    • Crommelin, D., & Edeling, W. (2021). Resampling with neural networks for stochastic parameterization in multiscale systems. Physica D, 422, Article 132894. https://doi.org/10.1016/j.physd.2021.132894 [details]
    • Edeling, W., Arabnejad, H., Sinclair, R., Suleimenova, D., Gopalakrishnan, K., Bosak, B., Groen, D., Mahmood, I., Crommelin, D., & Coveney, P. V. (2021). The impact of uncertainty on predictions of the CovidSim epidemiological code. Nature Computational Science, 1(2), 128-135. https://doi.org/10.1038/s43588-021-00028-9 [details]
    • Groen, D., Arabnejad, H., Jancauskas, V., Edeling, W. N., Jansson, F., Richardson, R. A., Lakhlili, J., Veen, L., Bosak, B., Kopta, P., Wright, D. W., Monnier, N., Karlshoefer, P., Suleimenova, D., Sinclair, R., Vassaux, M., Nikishova, A., Bieniek, M., Luk, O. O., ... Coveney, P. V. (2021). VECMAtk: a scalable verification, validation and uncertainty quantification toolkit for scientific simulations. Philosophical Transactions of the Royal Society A - Mathematical, Physical and Engineering Sciences, 379(2197), Article 20200221. https://doi.org/10.1098/rsta.2020.0221 [details]
    • Gugole, F., Coffeng, L. E., Edeling, W., Sanderse, B., de Vlas, S. J., & Crommelin, D. (2021). Uncertainty quantification and sensitivity analysis of COVID-19 exit strategies in an individual-based transmission model. PLoS Computational Biology, 17(9), Article 1009355. https://doi.org/10.1371/journal.pcbi.1009355 [details]
    • Jansson, F., Edeling, W., Attema, J., & Crommelin, D. (2021). Assessing uncertainties from physical parameters and modelling choices in an atmospheric large eddy simulation model. Philosophical Transactions of the Royal Society A - Mathematical, Physical and Engineering Sciences, 379(2197), Article 20200073. https://doi.org/10.1098/rsta.2020.0073 [details]
    • Suleimenova, D., Arabnejad, H., Edeling, W. N., Coster, D., Luk, O. O., Lakhlili, J., Jancauskas, V., Kulczewski, M., Veen, L., Ye, D., Zun, P., Krzhizhanovskaya, V., Hoekstra, A., Crommelin, D., Coveney, P. V., & Groen, D. (2021). Tutorial applications for Verification, Validation and Uncertainty Quantification using VECMA toolkit. Journal of Computational Science, 53, Article 101402. https://doi.org/10.1016/j.jocs.2021.101402 [details]
    • Verheul, N., & Crommelin, D. (2021). Stochastic parametrization with VARX processes. Communications in Applied Mathematics and Computational Science, 16(1), 33-57. https://doi.org/10.2140/camcos.2021.16.33 [details]
      • Downloads
    • van den Oord, G., Chertova, M., Jansson, F., Pelupessy, I., Siebesma, P., & Crommelin, D. (2021). Performance optimization and load-balancing modeling for superparametrization by 3D LES. In T. Robinson (Ed.), PASC '21: Proceedings of the Platform for Advanced Scientific Computing Conference Article 7 The Association for Computing Machinery. https://doi.org/10.1145/3468267.3470611 [details]

    2020

    • Edeling, W., & Crommelin, D. (2020). Reducing data-driven dynamical subgrid scale models by physical constraints. Computers and Fluids, 201, Article 104470. https://doi.org/10.1016/j.compfluid.2020.104470 [details]
    • Razaaly, N., Crommelin, D., & Congedo, P. M. (2020). Efficient estimation of extreme quantiles using adaptive kriging and importance sampling. International Journal for Numerical Methods in Engineering, 121(9), 2086-2105. https://doi.org/10.1002/nme.6300 [details]
    • Wright, D. W., Richardson, R. A., Edeling, W., Lakhlili, J., Sinclair, R. C., Jancauskas, V., Suleimenova, D., Bosak, B., Kulczewski, M., Piontek, T., Kopta, P., Chirca, I., Arabnejad, H., Luk, O. O., Hoenen, O., Węglarz, J., Crommelin, D., Groen, D., & Coveney, P. V. (2020). Building Confidence in Simulation: Applications of EasyVVUQ. Advanced Theory and Simulations, 3(8), Article 1900246. https://doi.org/10.1002/adts.201900246 [details]
    • van den Oord, G., Jansson, F., Pelupessy, I., Chertova, M., Grönqvist, J. H., Siebesma, P., & Crommelin, D. (2020). A Python interface to the Dutch Atmospheric Large-Eddy Simulation. SoftwareX, 12, Article 100608. https://doi.org/10.1016/j.softx.2020.100608 [details]

    2019

    • Bhaumik, D., Crommelin, D., Kapodistria, S., & Zwart, B. (2019). Hidden Markov Models for Wind Farm Power Output. IEEE Transactions on Sustainable Energy, 10(2), 533-539. https://doi.org/10.1109/TSTE.2018.2834475 [details]
    • Bisewski, K., Crommelin, D., & Mandjes, M. (2019). Rare event simulation for steady-state probabilities via recurrency cycles. Chaos, 29(3), Article 033131. https://doi.org/10.1063/1.5080296 [details]
    • Edeling, W., & Crommelin, D. (2019). Towards data-driven dynamic surrogate models for ocean flow. In Proceedings of the PASC19 Conference: Platform for Advanced Scientific Computing Conference : Zurich, Switzerland, 12-14 June 2019 Article 3 The Association for Computing Machinery. https://doi.org/10.1145/3324989.3325713 [details]
    • Eggels, A., & Crommelin, D. (2019). Quantifying data dependencies with Rényi mutual information and minimum spanning trees. Entropy, 21(2), Article 100. https://doi.org/10.3390/e21020100 [details]
    • Groen, D., Richardson, R. A., Wright, D. W., Jancauskas, V., Sinclair, R., Karlshoefer, P., Vassaux, M., Arabnejad, H., Piontek, T., Kopta, P., Bosak, B., Lakhlili, J., Hoenen, O., Suleimenova, D., Edeling, W., Crommelin, D., Nikishova, A., & Coveney, P. V. (2019). Introducing VECMAtk - Verification, Validation and Uncertainty Quantification for Multiscale and HPC Simulations. In J. M. F. Rodrigues, P. J. S. Cardoso, J. Monteiro, R. Lam, V. V. Krzhizhanovskaya, M. H. Lees, J. J. Dongarra, & P. M. A. Sloot (Eds.), Computational Science – ICCS 2019 : 19th International Conference Faro, Portugal, June 12-14, 2019 Proceedings (Vol. IV, pp. 479-492). (Lecture Notes in Computer Science; Vol. 11539). Springer. https://doi.org/10.1007/978-3-030-22747-0_36 [details]
    • Jansson, F., van den Oord, G., Pelupessy, I., Grönqvist, J. H., Siebesma, A. P., & Crommelin, D. (2019). Regional Superparameterization in a Global Circulation Model Using Large Eddy Simulations. Journal of Advances in Modeling Earth Systems, 11(9), 2958-2979. https://doi.org/10.1029/2018MS001600 [details]
    • Viebahn, J., Crommelin, D., & Dijkstra, H. (2019). Toward a Turbulence Closure Based on Energy Modes. Journal of Physical Oceanography, 49(4), 1075-1097. https://doi.org/10.1175/JPO-D-18-0117.1 [details]

    2018

    • Bhaumik, D., Crommelin, D., & Zwart, B. (2018). Mitigation of large power spills by an energy storage device in a stand alone energy system. Journal of Energy Storage, 16, 76-83. https://doi.org/10.1016/j.est.2017.12.012 [details]
    • Bisewski, K., Crommelin, D., & Mandjes, M. (2018). Controlling the time discretization bias for the supremum of brownian motion. ACM Transactions on Modeling and Computer Simulation, 28(3), Article 24. https://doi.org/10.1145/3177775 [details]
    • Bisewski, K., Crommelin, D., & Mandjes, M. (2018). Simulation-based assessment of the stationary tail distribution of a stochastic differential equation. In M. Rabe, A. A. Juan, N. Mustafee, A. Skoogh, S. Jain, & B. Johansson (Eds.), WSC'18: proceedings of the 2018 Winter Simulation Conference, December 9-12, 2018, Gothenburg, Sweden : Simulation for a noble cause (pp. 1742-1753). (Proceedings of the Winter Simulation Conference; Vol. 2018). IEEE. https://doi.org/10.1109/WSC.2018.8632197 [details]
    • Crommelin, D. (2018). Cellular Automata for Clouds and Convection. In P-Y. Louis, & F. R. Nardi (Eds.), Probabilistic Cellular Automata: Theory, Applications and Future Perspectives (pp. 327-339). (Emergence, Complexity and Computation; Vol. 27). Springer. https://doi.org/10.1007/978-3-319-65558-1_20 [details]
    • Eggels, A. W., & Crommelin, D. T. (2018). Uncertainty Quantification with dependent inputs: wind and waves. In R. Owen, R. de Borst, J. Reese, & C. Pearce (Eds.), Proceedings of the 6th. European Conference on Computational Mechanics (Solids, Structures and Coupled Problems ECCM 6, 7th. European Conference on Computational Fluid Dynamics ECFD 7, Glasgow, Scotland, UK, June 11-15, 2018 (pp. 4099-4110). International Center for Numerical Methods in Engineering. http://www.eccm-ecfd2018.org/frontal/docs/Ebook-Glasgow-2018-ECCM-VI-ECFD-VII.pdf [details]
    • Eggels, A. W., Crommelin, D. T., & Witteveen, J. A. S. (2018). Clustering-based collocation for uncertainty propagation with multivariate dependent inputs. International Journal for Uncertainty Quantification, 8(1), 43-59. https://doi.org/10.1615/Int.J.UncertaintyQuantification.2018020215 [details]

    2017

    • Berner, J., Achatz, U., Batté, L., Bengtsson, L., de la Cámara, A., Christensen, H. M., Colangeli, M., Coleman, D. R. B., Crommelin, D., Dolaptchiev, S. I., Franzke, C. L. E., Friederichs, P., Imkeller, P., Järvinen, H., Juricke, S., Kitsios, V., Lott, F., Lucarini, V., Mahajan, S., ... Yano, J-I. (2017). Stochastic Parameterization: Toward a New View of Weather and Climate Models. Bulletin of the American Meteorological Society, 98(3), 565-587. https://doi.org/10.1175/BAMS-D-15-00268.1 [details]
    • Gottwald, G. A., Crommelin, D. T., & Franzke, C. L. E. (2017). Stochastic climate theory. In C. L. E. Franzke, & T. J. O'Kane (Eds.), Nonlinear and Stochastic Climate Dynamics (pp. 209-240). Cambridge University Press. https://arxiv.org/abs/1612.07474 [details]
    • Verheul, N., Viebahn, J., & Crommelin, D. (2017). Covariate-based stochastic parameterization of baroclinic ocean eddies. Mathematics of Climate and Weather Forecasting, 3(1), 90-117. https://doi.org/10.1515/mcwf-2017-0005 [details]

    2016

    • Bhaumik, D., Crommelin, D., & Zwart, B. (2016). A computational method for optimizing storage placement to maximize power network reliability. In T. M. K. Roeder, P. I. Frazier, R. Szechtman, E. Zhou, T. Huschka, & S. E. Chick (Eds.), WSC'16 : Winter Simulation Conference: simulating complex service systems : Crystal Gateway Marriott, Arlington, VA, December 11-14, 2016 (pp. 883-894). IEEE. https://doi.org/10.1109/WSC.2016.7822150 [details]
    • Dorrestijn, J., Crommelin, D. T., Siebesma, A. P., Jonker, H. J. J., & Selten, F. (2016). Stochastic Convection Parameterization with Markov Chains in an Intermediate-Complexity GCM. Journal of the Atmospheric Sciences, 73(3), 1367-1382. Advance online publication. https://doi.org/10.1175/JAS-D-15-0244.1 [details]
    • Verheul, N., & Crommelin, D. (2016). Data-driven stochastic representations of unresolved features in multiscale models. Communications in Mathematical Sciences, 14(5), 1213 – 1236. https://doi.org/10.4310/CMS.2016.v14.n5.a2 [details]

    2015

    • Crommelin, D., & Khouider, B. (2015). Stochastic and Statistical Methods in Climate, Atmosphere, and Ocean Science. In B. Engquist (Ed.), Encyclopedia of Applied and Computational Mathematics (pp. 1377-1386). Springer Reference. https://doi.org/10.1007/978-3-540-70529-1_565 [details]

    2014

    • Thompson, W. F., Monahan, A. H., & Crommelin, D. (2014). Parametric Estimation of the Stochastic Dynamics of Sea Surface Winds. Journal of the Atmospheric Sciences, 71(9), 3465-3483. https://doi.org/10.1175/JAS-D-13-0260.1 [details]
    • Wadman, W., Crommelin, D., & Frank, J. (2014). A separated splitting technique for disconnected rare event sets. In A. Tolk, S. Y. Diallo, I. O. Ryzhov, L. Yilmaz, S. Buckley, & J. A. Miller (Eds.), Proceedings of the 2014 Winter Simulation Conference: exploring big data through simulation: December 7-10, 2014, Westin Savannah Harbor Resort, Savannah, GA (pp. 522-532). IEEE. https://doi.org/10.1109/WSC.2014.7019917 [details]

    2013

    • Wadman, W., Bloemhof, G., Crommelin, D., & Frank, J. (2013). Probablistic Power Flow Simulations Allowing Temporary Current Overloading. In A. Ozdemir (Ed.), Proceedings of the 12th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS 2012), 10-14th June 2012, Istanbul, Turkey (pp. 494-499). PAMPS. http://www.pmaps2012.itu.edu.tr/ [details]

    2020

    2018

    • Crommelin, D. (2018). Simulatie, chaos en onzekerheid. (Oratiereeks; No. 593). Universiteit van Amsterdam. [details]

    Journal editor

    2021

    • Arabnejad, H., Groen, D. J., Gopalakrishnan, K., Bosak, B., Crommelin, D., Coveney, P., Suleimenova, D., Edeling, W. & Mahmood, I. (2021). FabCovidSim. Zenodo. https://doi.org/10.5281/zenodo.4445290
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  • Ancillary activities
    • Centrum voor Wiskunde & Informatica
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