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Daan Crommelin
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- affiliation: Centrum Wiskunde & Informatica, Amsterdam, The Netherlands
- affiliation: University of Amsterdam, Korteweg-de Vries Institute for Mathematics, The Netherlands
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2020 – today
- 2024
- [i10]Rik Hoekstra, Daan Crommelin, Wouter Edeling:
Reduced Data-Driven Turbulence Closure for Capturing Long-Term Statistics. CoRR abs/2407.14132 (2024) - 2023
- [j11]Hugo Melchers, Daan Crommelin
, Barry Koren, Vlado Menkovski, Benjamin Sanderse
Comparison of neural closure models for discretised PDEs. Comput. Math. Appl. 143: 94-107 (2023) - 2022
- [i9]Hugo Melchers, Daan Crommelin, Barry Koren, Vlado Menkovski, Benjamin Sanderse:
Comparison of neural closure models for discretised PDEs. CoRR abs/2210.14675 (2022) - 2021
- [j10]Diana Suleimenova, Hamid Arabnejad, Wouter Edeling, David Coster, Onnie O. Luk, Jalal Lakhlili, Vytautas Jancauskas, Michal Kulczewski, Lourens E. Veen, Dongwei Ye
Tutorial applications for Verification, Validation and Uncertainty Quantification using VECMA toolkit. J. Comput. Sci. 53: 101402 (2021) - [j9]Wouter Edeling, Hamid Arabnejad
The impact of uncertainty on predictions of the CovidSim epidemiological code. Nat. Comput. Sci. 1(2): 128-135 (2021) - [j8]Federica Gugole
Uncertainty quantification and sensitivity analysis of COVID-19 exit strategies in an individual-based transmission model. PLoS Comput. Biol. 17(9) (2021) - [c10]Gijs van den Oord, Maria Chertova, Fredrik Jansson
Performance optimization and load-balancing modeling for superparametrization by 3D LES. PASC 2021: 7:1-7:8 - [i8]Wouter Edeling, Daan Crommelin:
The Case for Uncertainty in COVID19 Predictions. ERCIM News 2021(124) (2021) - 2020
- [j7]Gijs van den Oord, Fredrik Jansson
A Python interface to the Dutch Atmospheric Large-Eddy Simulation. SoftwareX 12: 100608 (2020) - [i7]Debarati Bhaumik, Marko A. A. Boon, Daan Crommelin, Barry Koren, Bert Zwart:
Computing first passage times for Markov-modulated fluid models using numerical PDE problem solvers. CoRR abs/2003.14300 (2020) - [i6]Daan Crommelin, Wouter Edeling:
Resampling with neural networks for stochastic parameterization in multiscale systems. CoRR abs/2004.01457 (2020) - [i5]Nick Verheul, Daan Crommelin:
Stochastic parameterization with VARX processes. CoRR abs/2010.03293 (2020) - [i4]Derek Groen, Hamid Arabnejad, Vytautas Jancauskas, Wouter Edeling, Fredrik Jansson, Robin A. Richardson, Jalal Lakhlili, Linda van Veen, Bartosz Bosak, Piotr Kopta, D. W. Wright, Nicolas Monnier, Paul Karlshoefer, Diana Suleimenova, Robert Sinclair, Maxime Vassaux, Anna Nikishova, Mariusz Bieniek, Onnie O. Luk, Michal Kulczewski, Erwan Raffin, Daan Crommelin, Olivier Hoenen, David P. Coster, Tomasz Piontek, Peter V. Coveney:
VECMAtk: A Scalable Verification, Validation and Uncertainty Quantification Toolkit for Scientific Simulations. CoRR abs/2010.03923 (2020) - [i3]Daan Crommelin, Wouter Edeling, Fredrik Jansson:
Tackling the Multiscale Challenge of Climate Modelling. ERCIM News 2020(121) (2020)
2010 – 2019
- 2019
- [j6]Anne Eggels, Daan Crommelin
Quantifying Data Dependencies with Rényi Mutual Information and Minimum Spanning Trees. Entropy 21(2): 100 (2019) - [c9]F. Inti Pelupessy, Simon Portegies Zwart, Arjen van Elteren, Henk A. Dijkstra, Fredrik Jansson
Creating a Reusable Cross-Disciplinary Multi-scale and Multi-physics Framework: From AMUSE to OMUSE and Beyond. ICCS (4) 2019: 379-392 - [c8]Derek Groen, Robin A. Richardson, David W. Wright, Vytautas Jancauskas, Robert Sinclair
Introducing VECMAtk - Verification, Validation and Uncertainty Quantification for Multiscale and HPC Simulations. ICCS (4) 2019: 479-492 - [c7]Wouter Edeling, Daan Crommelin
Towards Data-Driven Dynamic Surrogate Models for Ocean Flow. PASC 2019: 3:1-3:10 - 2018
- [j5]Krzysztof Bisewski
Controlling the Time Discretization Bias for the Supremum of Brownian Motion. ACM Trans. Model. Comput. Simul. 28(3): 24:1-24:25 (2018) - [c6]Fredrik Jansson, Gijs van den Oord, Pier Siebesma, Daan Crommelin
Resolving Clouds in a Global Atmosphere Model - A Multiscale Approach with Nested Models. eScience 2018: 270 - [c5]Krzysztof Bisewski, Daan Crommelin
Simulation-based Assessment of the stationary Tail Distribution of a stochastic differential equation. WSC 2018: 1742-1753 - 2016
- [j4]Wander S. Wadman
A Large-Deviation-Based Splitting Estimation of Power Flow Reliability. ACM Trans. Model. Comput. Simul. 26(4): 23:1-23:26 (2016) - [c4]Debarati Bhaumik
A computational method for optimizing storage placement to maximize power network reliability. WSC 2016: 883-894 - 2015
- [c3]Debarati Bhaumik
Efficient estimator of probabilities of large power spills in an stand-alone system with wind generation and storage. WSC 2015: 3178-3179 - 2014
- [c2]Wander Wadman, Daan Crommelin
A separated splitting technique for disconnected rare event sets. WSC 2014: 522-532 - 2013
- [c1]Wander Wadman, Daan Crommelin
Applying a splitting technique to estimate electrical grid reliability. WSC 2013: 577-588 - 2011
- [j3]Daan Crommelin
Diffusion Estimation from Multiscale Data by Operator Eigenpairs. Multiscale Model. Simul. 9(4): 1588-1623 (2011) - 2010
- [i2]Daan Crommelin, Jason Frank:
Stochastics and Computation for Turbulent Systems. ERCIM News 2010(81) (2010)
2000 – 2009
- 2009
- [j2]Daan Crommelin
Data-Based Inference of Generators for Markov Jump Processes Using Convex Optimization. Multiscale Model. Simul. 7(4): 1751-1778 (2009) - 2008
- [i1]Daan Crommelin:
Clouds by Chance: Improving Atmosphere Models with Random Numbers. ERCIM News 2008(73) (2008) - 2006
- [j1]Daan Crommelin
Fitting timeseries by continuous-time Markov chains: A quadratic programming approach. J. Comput. Phys. 217(2): 782-805 (2006)
Coauthor Index
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last updated on 2024-08-25 19:07 CEST by the dblp team
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