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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

    2022

    2021

    • Crommelin, D., & Edeling, W. (2021). Resampling with neural networks for stochastic parameterization in multiscale systems. Physica D, 422, [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), [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), [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), [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, [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 [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, [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), [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, [100608]. https://doi.org/10.1016/j.softx.2020.100608 [details]

    2019

    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), [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). Piscataway, NJ: 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). Barcelona: International Center for Numerical Methods in Engineering. [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., ... 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: Cambridge University Press. [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). Piscataway, NJ: 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. 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). Heidelberg: 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). Piscataway, NJ: 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). Istanbul: PAMPS. [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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    • Centrum voor Wiskunde & Informatica
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