Search results

  • Full text

  • Document type

    • Publication year

  • Organisation

Results: 46
Number of items: 46
  • Open Access
    Hoekstra, R., Crommelin, D., & Edeling, W. (2024). Reduced data-driven turbulence closure for capturing long-term statistics. Computers and Fluids, 285, Article 106469. https://doi.org/10.1016/j.compfluid.2024.106469
  • Open Access
    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
  • Open Access
    Melchers, H., Crommelin, D., Koren, B., Menkovski, V., & Sanderse, B. (2023). Comparison of neural closure models for discretised PDEs. Computers and Mathematics with Applications, 143, 94-107. https://doi.org/10.1016/j.camwa.2023.04.030
  • Open Access
    Verheul, N. (2022). Stochastic models for unresolved scales in ocean flows. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Jansson, F., van den Oord, G., Pelupessy, I., Chertova, M., Grönqvist, J. H., Siebesma, A. P., & Crommelin, D. (2022). Representing Cloud Mesoscale Variability in Superparameterized Climate Models. Journal of Advances in Modeling Earth Systems, 14(8), Article e2021MS002892. https://doi.org/10.1029/2021MS002892
  • 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
  • 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
    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
  • Open Access
    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
  • Open Access
    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
Page 1 of 5