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Results: 46
Number of items: 46
  • 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
  • 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
  • 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
  • Open Access
    Eggels, A. W. (2019). Uncertainty quantification with dependent input data: Including applications to offshore wind farms. [Thesis, externally prepared, Universiteit van Amsterdam].
  • Open Access
    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
  • Open Access
    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
  • Open Access
    Bisewski, K. L. (2019). Rare event simulation and time discretization. [Thesis, fully internal, Universiteit van Amsterdam].
  • 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
  • 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
  • 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
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