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Results: 46
Number of items: 46
  • Open Access
    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
  • Open Access
    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
  • Open Access
    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
  • 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
  • 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
  • Open Access
    Crommelin, D. T., Edeling, W., & Jansson, F. (2020). Tackling the Multiscale Challenge of Climate Modelling. ERCIM News, 121, 15-17. https://ercim-news.ercim.eu/en121
  • Open Access
    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
  • Open Access
    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
  • 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
  • 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
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