R. Herkert. Software, (2024)Related to: R. Herkert, P. Buchfink, B. Haasdonk, J. Rettberg, J. Fehr. (2024), "Error Analysis of Randomized Symplectic Model Order Reduction for Hamiltonian systems". arXiv: 2405.10465.
R. Herkert. Software, (2023)Related to: R. Herkert, P. Buchfink, B. Haasdonk, J. Rettberg, J. Fehr: Randomized Symplectic Model Order Reduction for Hamiltonian Systemsm 2023. arXiv: 2303.04036.
R. Herkert. Software, (2024)Related to: R. Herkert, P. Buchfink, T. Wenzel, B. Haasdonk, P. Toktaliev, O. Iliev (2024), "Greedy Kernel Methods for Approximating Breakthrough Curves for Reactive Flow from 3D Porous Geometry Data". arXiv: 2405.19170.
S. Hermann. Dataset, (2022)Related to: Hermann, S., Fehr, J. Documenting research software in engineering science. Sci Rep 12, 6567 (2022). doi: 10.1038/s41598-022-10376-9.
D. Holzmüller. Software, (2022)Related to: David Holzmüller and Dirk Pflüger. Fast Sparse Grid Operations using the Unidirectional Principle: A Generalized and Unified Framework. Sparse Grids and Applications - Munich 2018 (2021). doi: 10.1007/978-3-030-81362-8_4.
D. Holzmüller. Software, (2021)Related to: David Holzmüller. On the Universality of the Double Descent Peak in Ridgeless Regression. International Conference on Learning Representations, 2021. arXiv: 2010.01851.
D. Holzmüller, V. Zaverkin, J. Kästner, and I. Steinwart. Software, (2023)Related to: David Holzmüller, Viktor Zaverkin, Johannes Kästner, and Ingo Steinwart. A Framework and Benchmark for Deep Batch Active Learning for Regression, 2023. arXiv: 2203.09410.
D. Holzmüller, V. Zaverkin, J. Kästner, and I. Steinwart. Software, (2022)Related to: David, Holzmüller, Viktor Zaverkin, Johannes Kästner, and Ingo Steinwart. A Framework and Benchmark for Deep Batch Active Learning for Regression, 2022. arXiv: 2203.09410.
D. Holzmüller, V. Zaverkin, J. Kästner, and I. Steinwart. Software, (2022)Related to: David Holzmüller, Viktor Zaverkin, Johannes Kästner, and Ingo Steinwart. A Framework and Benchmark for Deep Batch Active Learning for Regression, 2022. arXiv: 2203.09410.
C. Homs Pons, and R. Lautenschlager. Software, (2024)Related to: Coupled Simulations and Parameter Inversion for Neural System and Electrophysiological Muscle Models, submitted to GAMM Mitteilungen.
H. Hsueh. Dataset, (2021)Related to: Han-Fang Hsueh, Anneli Guthke, Thomas Wöhling, Wolfgang Nowak: Diagnosis of model-structural errors with a sliding time-window Bayesian analysis. In: Water Resource Research (submitted). arXiv: 2107.09399.
F. Huber, P. Bürkner, D. Göddeke, and M. Schulte. Dataset, (2023)Related to: Huber, Felix; Bürkner, Paul-Christian; Göddeke, Dominik; Schulte, MiriamKnowledge-Based Modeling of Simulation Behavior for Bayesian OptimizationComputational Mechanics (submitted).
H. Jäger. Software, (2023)Related to: Jäger, Henrik, Alexander Schlaich, Jie Yang, Cheng Lian, Svyatoslav Kondrat und Christian Holm. 2023. A screening of results on the decay length in concentrated electrolytes. Faraday Discussions. Faraday Discussions (Februar). doi: 10.1039/d3fd00043e.
M. Kelm, C. Bringedal, and B. Flemisch. Dataset, (2023)Related to: Kelm, M., Gärttner, S., Bringedal, C. et al. Comparison study of phase-field and level-set method for three-phase systems including two minerals. Comput Geosci 26, 545-570 (2022). doi: 10.1007/s10596-022-10142-w.
F. Kempter, L. Lantella, N. Stutzig, J. Fehr, and T. Siebert. Software, (2023)Related to: Kempter, Fabian; Lantella, Lorena; Stutzig, Norman; Fehr, Jörg and Siebert, Tobias: Role of Rotated Head Postures on Volunteer Kinematics and Muscle Activity in Braking Scenarios Performed on a Driving Simulator, Annals of Biomedical Engineering , Vol. 51, No. 4 p. 771-782 2023. doi: 10.1007/s10439-022-03087-9.
J. Kneifl, and J. Fehr. Software, (2020)Related to: Jonas Kneifl, Dennis Grunert, and Joerg Fehr (2021). A non-intrusive nonlinear model reduction method for structural dynamical problems based on machine learning. In: International Journal for Numerical Methods in Engineering, 122:4774-4786. doi: 10.1002/nme.6712.
J. Kneifl, and J. Fehr. Software, (2023)Related to: Kneifl, J., Kutz, J. N., Brunton, S.L., Fehr, J.: Multi-Hierarchical Surrogate Learning of Structural Dynamical Systems Using Graph Convolutional Neural Networks. To be submitted (2023).
J. Kneifl, D. Rosin, O. Avci, O. Röhrle, and J. Fehr. Software, (2023)Related to: Kneifl, J, Rosin, D., Röhrle, O., Fehr, J.: Low-dimensional Data-based Surrogate Model of a Continuum-mechanical Musculoskeletal System Based on Non-intrusive Model Order Reduction, 2022. tbd.
P. Kurzeja, and H. Steeb. Philosophical transactions of the Royal Society. Series A, Mathematical, physical and engineering sciences, 380 (2237):
20210370(2022)
R. Leiteritz, M. Hurler, and D. Pflüger. 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA), page 1668-1673. Piscataway, IEEE, (2021)
J. Magiera. Dataset, (2024)Related to: Jim Magiera, Deep Ray, Jan S. Hesthaven, Christian Rohde, Constraint-aware neural networks for Riemann problems, Journal of Computational Physics, Volume 409, 2020, 109345. doi: 10.1016/j.jcp.2020.109345.
T. Munz, R. Garcia, and D. Weiskopf. Software, (2021)Related to: R. Garcia, T. Munz, and D. Weiskopf. "Visual Analytics Tool for the Interpretation of Hidden States in Recurrent Neural Networks". Visual Computing for Industry, Biomedicine, and Art (VCIBA). 2021. doi: 10.1186/s42492-021-00090-0.
T. Munz, N. Schäfer, T. Blascheck, K. Kurzhals, E. Zhang, and D. Weiskopf. Software, (2020)Related to: T. Munz, N. Schäfer, T. Blascheck, K. Kurzhals, E. Zhang, and D. Weiskopf. "Comparative Visual Gaze Analysis for Virtual Board Games". Proceedings of the 13th International Symposium on Visual Information Communication and Interaction (VINCI 2020). 2020. DOI: 10.1145/3430036.3430038.
T. Munz, D. Väth, P. Kuznecov, N. Vu, and D. Weiskopf. Software, (2021)Related to: T. Munz, D. Väth, P. Kuznecov, N. T. Vu, and D. Weiskopf. "Visual-Interactive Neural Machine Translation". Graphics Interface. 2021.
T. Munz, D. Väth, P. Kuznecov, N. Vu, and D. Weiskopf. Dataset, (2021)Related to: T. Munz, D. Väth, P. Kuznecov, N. T. Vu, and D. Weiskopf. "Visual-Interactive Neural Machine Translation". Graphics Interface. 2021.
T. Munz, D. Väth, P. Kuznecov, N. Vu, and D. Weiskopf. Software, (2022)Related to: T. Munz, D. Väth, P. Kuznecov, N. T. Vu, and D. Weiskopf. "Visualization-based improvement of neural machine translation", Computers & Graphics, 2021. doi: 10.1016/j.cag.2021.12.003.
T. Munz-Körner, S. Künzel, and D. Weiskopf. Dataset, (2023)Related to: T. Munz-Körner, S. Künzel, and D. Weiskopf. "Visual-Explainable AI: The Use Case of Language Models". International Conference on Data-Integrated Simulation Science (SimTech2023). 2023.
T. Munz-Körner, and D. Weiskopf. Dataset, (2024)Related to: T. Munz-Körner, D. Weiskopf, Exploring visual quality of multidimensional time series projections, Visual Informatics (2024). doi: 10.1016/j.visinf.2024.04.004.
T. Munz-Körner, and D. Weiskopf. Software, (2024)Related to: T. Munz-Körner, D. Weiskopf, Exploring visual quality of multidimensional time series projections, Visual Informatics (2024). doi: 10.1016/j.visinf.2024.04.004.
J. Nicodemus, J. Kneifl, J. Fehr, and B. Unger. 10th Vienna International Conference on Mathematical Modelling MATHMOD 2022, 55, 20, page 331-336. Elsevier, (2022)
L. Nölle, P. Lerge, O. Martynenko, I. Wochner, F. Kempter, C. Kleinbach, S. Schmitt, and J. Fehr. Dataset, (2022)Related to: Kleinbach, C., Martynenko, O., Promies, J., Haeufle, D.F., Fehr, J., Schmitt, S., 2017. Implementation and validation of the extended hill-type muscle model with robust routing capabilities in LS-DYNA for active human body models. Biomedical engineering online 16, 109. doi: 10.1186/s12938-017-0399-7.
J. Pelzer. Dataset, (2023)Related to: Pelzer, Julia, and Miriam Schulte. "Efficient two-stage modeling of heat plume interactions of geothermal heat pumps in shallow aquifers using convolutional neural networks." Geoenergy Science and Engineering (2024): 212788. doi: 10.1016/j.geoen.2024.212788.
J. Pelzer. Software, (2024)Related to: Pelzer, Julia, and Miriam Schulte. "Efficient two-stage modeling of heat plume interactions of geothermal heat pumps in shallow aquifers using convolutional neural networks." Geoenergy Science and Engineering (2024): 212788. doi: 10.1016/j.geoen.2024.212788.
J. Pelzer. Software, (2024)Related to: Pelzer, Julia, and Miriam Schulte. "Efficient two-stage modeling of heat plume interactions of geothermal heat pumps in shallow aquifers using convolutional neural networks." Geoenergy Science and Engineering (2024): 212788. doi: 10.1016/j.geoen.2024.212788.
J. Pelzer. Dataset, (2023)Related to: Pelzer, Julia, and Miriam Schulte. "Efficient two-stage modeling of heat plume interactions of geothermal heat pumps in shallow aquifers using convolutional neural networks." Geoenergy Science and Engineering (2024): 212788. doi: 10.1016/j.geoen.2024.212788.
J. Pelzer. Dataset, (2023)Related to: Pelzer, Julia, and Miriam Schulte. "Efficient two-stage modeling of heat plume interactions of geothermal heat pumps in shallow aquifers using convolutional neural networks." Geoenergy Science and Engineering (2024): 212788. doi: 10.1016/j.geoen.2024.212788.
J. Pelzer. Dataset, (2023)Related to: Pelzer, Julia, and Miriam Schulte. "Efficient two-stage modeling of heat plume interactions of geothermal heat pumps in shallow aquifers using convolutional neural networks." Geoenergy Science and Engineering (2024): 212788. doi: 10.1016/j.geoen.2024.212788.
J. Potyka, and K. Schulte. Dataset, (2023)Related to: Johanna Potyka and Kathrin Schulte: A volume of fluid method for three dimensional direct numerical simulations of immiscible droplet collisions, International Journal of Multiphase Flow, Volume 170, 2024, 104654. doi: 10.1016/j.ijmultiphaseflow.2023.104654.
J. Potyka, K. Schulte, and C. Planchette. Dataset, (2023)Related to: Johanna Potyka, Kathrin Schulte and Carole Planchette; Liquid distribution after head-on separation of two colliding immiscible liquid droplets. Physics of Fluids, 2023; 35 (10): 102125. doi: 10.1063/5.0168080.