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).
T. Pollinger. Dataset, (2023)Related to: Leveraging the compute power of two HPC systems for higher-dimensional grid-based simulations with the widely-distributed sparse grid combination technique (submitted).
A. Iurshina, J. Pan, R. Boutalbi, and S. Staab. Dataset, (2023)Related to: Anastasiia Iurshina, Jiaxin Pan, Rafika Boutalbi, and Steffen Staab. 2022. NILK: Entity Linking Dataset Targeting NIL-linking Cases. In: Proceedings of the 31st ACM International Conference on Information & Knowledge Management (CIKM '22). Association for Computing Machinery, New York, NY, USA, 4069-4073. doi: 10.1145/3511808.3557659.
L. Willeke, D. Schneider, and B. Uekermann. Software, (2023)Related to: Willeke, Leonard; Schneider, David; Uekermann, Benjamin; 2023; Ä preCICE-FMI runner to Couple FMUs to PDE-Based Simulations" (submitted).
T. Schrader. Software, (2023)Related to: Schrader, Timo Pierre, "Efficient Application of Accelerator Cards for the Coupling Library preCICE", Master’s Thesis, Informatics, University of Stuttgart, 2023. doi: 10.18419/opus-13027.
L. Willeke. Software, (2023)Related to: L.Willeke, A preCICE-FMI Runner to couple controller models to PDEs, Master Thesis, University of Stuttgart, 2023. doi: 10.18419/opus-13130.
A. Baier, and D. Frank. Software, (2023)Related to: Baier, Alexandra, Boukhers, Zeyd, & Staab, Steffen (2021). Hybrid Physics and Deep Learning Model for Interpretable Vehicle State Prediction. ArXiv, abs/2103.06727. arXiv: abs/2103.06727.
A. Baier, D. Aspandi Latif, and S. Staab. Software, (2023)Related to: Alexandra Baier, Decky Aspandi and Steffen Staab, "ReLiNet: Stable and Explainable Multistep Prediction with Recurrent Linear Parameter Varying Networks", Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence (IJCAI-23), 2023.
T. Pollinger. Software, (2022)Related to: Pollinger, T., Rentrop, J., Pflüger, D. & Kormann, K. (2022). A mass-conserving sparse grid combination technique with biorthogonal hierarchical basis functions for kinetic simulations arXiv. doi: 10.48550/arXiv.2209.14064.
M. Takamoto, T. Praditia, R. Leiteritz, D. MacKinlay, F. Alesiani, D. Pflüger, and M. Niepert. Dataset, (2022)Related to: Takamoto, M., Praditia, T., Leiteritz, R., MacKinlay, D., Alesiani, F., Pflüger, D. and Niepert, M.: PDEBench: An Extensive Benchmark for Scientific Machine Learning. submitted to the 36th Conference on Neural Information Processing Systems (NeurIPS 2022) Track on Datasets and Benchmarks. arXiv: 2210.07182.
M. Takamoto, T. Praditia, R. Leiteritz, D. MacKinlay, F. Alesiani, D. Pflüger, and M. Niepert. Dataset, (2022)Related to: Takamoto, M., Praditia, T., Leiteritz, R., MacKinlay, D., Alesiani, F., Pflüger, D. and Niepert, M.: PDEBench: An Extensive Benchmark for Scientific Machine Learning. submitted to the 36th Conference on Neural Information Processing Systems (NeurIPS 2022) Track on Datasets and Benchmarks.
A. Baier, and S. Staab. Dataset, (2022)Related to: Baier, A., Boukhers, Z., & Staab, S. (2021). Hybrid Physics and Deep Learning Model for Interpretable Vehicle State Prediction. arXiv: 2103.06727.