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. Craen, M. Breyer, and D. Pflüger. 2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), page 818-827. Piscataway, IEEE, (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)
P. Domanski, D. Pflüger, R. Latty, and J. Rivoir. 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA), page 1357-1364. Piscataway, IEEE, (2021)
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.