Fitting timeseries by continuous-time Markov chains: A quadratic programming approach. - Daan Crommelin; Eric Vanden-Eijnden (J. Comput. Phys., 2006)

Clouds by Chance: Improving Atmosphere Models with Random Numbers. - Daan Crommelin (ERCIM News, 2008)

Data-Based Inference of Generators for Markov Jump Processes Using Convex Optimization. - Daan Crommelin; Eric Vanden-Eijnden (Multiscale Model. Simul., 2009)

Stochastics and Computation for Turbulent Systems. - Daan Crommelin; Jason Frank (ERCIM News, 2010)

Diffusion Estimation from Multiscale Data by Operator Eigenpairs. - Daan Crommelin; Eric Vanden-Eijnden (Multiscale Model. Simul., 2011)

Applying a splitting technique to estimate electrical grid reliability. - Wander Wadman; Daan Crommelin; Jason Frank (WSC, 2013)

A separated splitting technique for disconnected rare event sets. - Wander Wadman; Daan Crommelin; Jason Frank (WSC, 2014)

Efficient estimator of probabilities of large power spills in an stand-alone system with wind generation and storage. - Debarati Bhaumik; Daan Crommelin; Bert Zwart (WSC, 2015)

A computational method for optimizing storage placement to maximize power network reliability. - Debarati Bhaumik; Daan Crommelin; Bert Zwart (WSC, 2016)

A Large-Deviation-Based Splitting Estimation of Power Flow Reliability. - Wander S. Wadman; Daan T. Crommelin; Bert P. Zwart (ACM Trans. Model. Comput. Simul., 2016)

Resolving Clouds in a Global Atmosphere Model - A Multiscale Approach with Nested Models. - Fredrik Jansson; Gijs van den Oord; Pier Siebesma; Daan Crommelin (eScience, 2018)

Simulation-based Assessment of the stationary Tail Distribution of a stochastic differential equation. - Krzysztof Bisewski; Daan Crommelin; Michel Mandjes (WSC, 2018)

Controlling the Time Discretization Bias for the Supremum of Brownian Motion. - Krzysztof Bisewski; Daan Crommelin; Michel Mandjes (ACM Trans. Model. Comput. Simul., 2018)

Introducing VECMAtk - Verification, Validation and Uncertainty Quantification for Multiscale and HPC Simulations. - Derek Groen; Robin A. Richardson; David W. Wright 001; Vytautas Jancauskas; Robert Sinclair; Paul Karlshoefer; Maxime Vassaux; Hamid Arabnejad; Tomasz Piontek; Piotr Kopta; Bartosz Bosak; Jalal Lakhlili; et al. (ICCS (4), 2019)

Creating a Reusable Cross-Disciplinary Multi-scale and Multi-physics Framework: From AMUSE to OMUSE and Beyond. - F. Inti Pelupessy; Simon Portegies Zwart; Arjen van Elteren; Henk A. Dijkstra; Fredrik Jansson; Daan Crommelin; Pier Siebesma; Ben van Werkhoven; Gijs van den Oord (ICCS (4), 2019)

Towards Data-Driven Dynamic Surrogate Models for Ocean Flow. - Wouter Edeling; Daan Crommelin (PASC, 2019)

Quantifying Data Dependencies with Rényi Mutual Information and Minimum Spanning Trees. - Anne Eggels; Daan Crommelin (Entropy, 2019)

Computing first passage times for Markov-modulated fluid models using numerical PDE problem solvers. - Debarati Bhaumik; Marko A. A. Boon; Daan Crommelin; Barry Koren; Bert Zwart (CoRR, 2020)

Resampling with neural networks for stochastic parameterization in multiscale systems. - Daan Crommelin; Wouter Edeling (CoRR, 2020)

Stochastic parameterization with VARX processes. - Nick Verheul; Daan Crommelin (CoRR, 2020)

VECMAtk: A Scalable Verification, Validation and Uncertainty Quantification Toolkit for Scientific Simulations. - 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; et al. (CoRR, 2020)

Tackling the Multiscale Challenge of Climate Modelling. - Daan Crommelin; Wouter Edeling; Fredrik Jansson (ERCIM News, 2020)

A Python interface to the Dutch Atmospheric Large-Eddy Simulation. - Gijs van den Oord; Fredrik Jansson; F. Inti Pelupessy; Maria Chertova; Johanna H. Grönqvist; Pier Siebesma; Daan Crommelin (SoftwareX, 2020)

Performance optimization and load-balancing modeling for superparametrization by 3D LES. - Gijs van den Oord; Maria Chertova; Fredrik Jansson; F. Inti Pelupessy; Pier Siebesma; Daan Crommelin (PASC, 2021)

The Case for Uncertainty in COVID19 Predictions. - Wouter Edeling; Daan Crommelin (ERCIM News, 2021)

Tutorial applications for Verification, Validation and Uncertainty Quantification using VECMA toolkit. - Diana Suleimenova; Hamid Arabnejad; Wouter Edeling; David Coster; Onnie O. Luk; Jalal Lakhlili; Vytautas Jancauskas; Michal Kulczewski; Lourens E. Veen; Dongwei Ye; Pavel Zun; Valeria V. Krzhizhanovskaya; et al. (J. Comput. Sci., 2021)

The impact of uncertainty on predictions of the CovidSim epidemiological code. - Wouter Edeling; Hamid Arabnejad; Robert Sinclair; Diana Suleimenova; Krishnakumar Gopalakrishnan; Bartosz Bosak; Derek Groen; Imran Mahmood; Daan Crommelin; Peter V. Coveney (Nat. Comput. Sci., 2021)

Uncertainty quantification and sensitivity analysis of COVID-19 exit strategies in an individual-based transmission model. - Federica Gugole; Luc E. Coffeng; Wouter Edeling; Benjamin Sanderse; Sake J. De Vlas; Daan Crommelin (PLoS Comput. Biol., 2021)

Comparison of neural closure models for discretised PDEs. - Hugo Melchers; Daan Crommelin; Barry Koren; Vlado Menkovski; Benjamin Sanderse (Comput. Math. Appl., 2023)

Reduced Data-Driven Turbulence Closure for Capturing Long-Term Statistics. - Rik Hoekstra; Daan Crommelin; Wouter Edeling (CoRR, 2024)