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Datasets and executables of data-driven uncertainty quantification benchmark in carbon dioxide storage

M. Köppel, F. Franzelin, I. Kröker, S. Oladyshkin, D. Wittwar, G. Santin, A. Barth, B. Haasdonk, W. Nowak, D. Pflüger, und C. Rohde. (November 2017)
DOI: 10.5281/zenodo.933827

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Fabian Hampp University of Stuttgart

Nuisance - OOFObjs : Database of out-of-focus objects (measurement nuisance) for generating synthetic training dataB. Jose, und F. Hampp. Dataset, (2025)Related to: Basil Jose, Fabian Hampp (2024). Machine learning based spray process quantification. International Journal of Multiphase Flow 172, 104702. doi: 10.1016/j.ijmultiphaseflow.2023.104702.

Nuisance - OOFObjs : Database of out-of-focus objects (measurement nuisance) for generating synthetic training dataB. Jose, und F. Hampp. Dataset, (2025)Related to: Basil Jose, Fabian Hampp (2024). Machine learning based spray process quantification. International Journal of Multiphase Flow 172, 104702. doi: 10.1016/j.ijmultiphaseflow.2023.104702.QoI - Soot filaments : Database of soot filaments image snippets for generating synthetic training dataB. Jose, K. Geigle, und F. Hampp. Dataset, (2025)QoI - Droplets : Database of droplets (quantity of interest) for generating synthetic training dataB. Jose, und F. Hampp. Dataset, (2025)Related to: Basil Jose, Fabian Hampp (2024). Machine learning based spray process quantification. International Journal of Multiphase Flow 172, 104702. doi: 10.1016/j.ijmultiphaseflow.2023.104702.

Dr. Fabian Schubö University of Stuttgart

Fabian Schnelle

Design and programming for cable-driven parallel robots in the German pavilion at the EXPO 2015P. Tempel, F. Schnelle, A. Pott, und P. Eberhard. Machines, 3 (3): 223-241 (2015)

Fabian Silber University of Stuttgart

Fabian Marquardt University of Stuttgart

Weitere Publikationen von Autoren mit dem selben Namen

Non-intrusive uncertainty quantification with sparse grids for multivariate peridynamic simulationsF. Franzelin, P. Diehl, D. Pflüger, und M. Schweitzer. Meshfree Methods for Partial Differential Equations VII, Volume 100 von Lecture Notes in Computational Science and Engineering, Sonstige, (Januar 2014)Comparison of data-driven uncertainty quantification methods for a carbon dioxide storage benchmark scenarioM. Köppel, F. Franzelin, I. Kröker, S. Oladyshkin, G. Santin, D. Wittwar, A. Barth, B. Haasdonk, W. Nowak, D. Pflüger und 1 andere Autor(en). Computational geosciences, (2019)Datasets and executables of data-driven uncertainty quantification benchmark in carbon dioxide storageM. Köppel, F. Franzelin, I. Kröker, S. Oladyshkin, D. Wittwar, G. Santin, A. Barth, B. Haasdonk, W. Nowak, D. Pflüger und 1 andere Autor(en). Dataset, (2019)Comparison of data-driven uncertainty quantification methods for a carbon dioxide storage benchmark scenario.M. Köppel, F. Franzelin, I. Kröker, S. Oladyshkin, G. Santin, D. Wittwar, A. Barth, B. Haasdonk, W. Nowak, D. Pflüger und 1 andere Autor(en). CoRR, (2018)Classification with Probability Density Estimation on Sparse GridsB. Peherstorfer, F. Franzelin, D. Pflüger, und H. Bungartz. Sparse Grids and Applications - Munich 2012, Volume 97 von Lecture Notes in Computational Science and Engineering, Seite 255--270. Springer International Publishing, (Januar 2014)Non-intrusive uncertainty quantification with sparse grids for multivariate peridynamic simulationsF. Franzelin, P. Diehl, und D. Pflüger. Meshfree methods for partial differential equations, Volume 7 von Lecture notes in computational science and engineering, Seite 115-143. Cham, Springer, (2015)Limiting Ranges of Function Values of Sparse Grid SurrogatesF. Franzelin, und D. Pflüger. Sparse grids and applications - Miami 2016, 123, Seite 69-91. Cham, Springer, (2018)Data-Driven Uncertainty Quantification for Large-Scale Simulations.F. Franzelin. University of Stuttgart, Germany, (2018)Bond-based peridynamics: a quantitative study of Mode I crack openingP. Diehl, F. Franzelin, D. Pflüger, und G. Ganzenmüller. International Journal of Fracture, (Mai 2016)B-Splines on Sparse Grids for Surrogates in Uncertainty QuantificationM. Rehme, F. Franzelin, und D. Pflüger. (2019)