Dieses Bild zeigtRaphael Leiteritz

Raphael Leiteritz

Herr M.Sc.

Wissenschaftlicher Angestellter
IPVS
Scientific Computing

Kontakt

Deutschland

Sprechstunde

Nach Vereinbarung

  1. Takamoto, M., Praditia, T., Leiteritz, R., MacKinlay, D., Alesiani, F., Pflüger, D., Niepert, M.: PDEBench Datasets. (2022). https://doi.org/10.18419/darus-2986.
  2. Leiteritz, R., Davis, K., Schulte, M., Pflüger, D.: Deep Learning-Based Surrogate Modelling of Thermal Plumes for Shallow Subsurface Temperature Approximation. In: AI for Earth Sciences ICLR Workshop 2022 (2022).
  3. Leiteritz, R., Davis, K., Schulte, M., Pflüger, D.: A Deep Learning Approach for Thermal Plume Prediction of Groundwater Heat Pumps, https://arxiv.org/abs/2203.14961, (2022). https://doi.org/10.48550/ARXIV.2203.14961.
  4. Leiteritz, R., Buchfink, P., Haasdonk, B., Pflüger, D.: Surrogate-data-enriched Physics-Aware Neural Networks. In: Proceedings of the Northern Lights Deep Learning Workshop 2022 (2022). https://doi.org/10.7557/18.6268.
  5. Takamoto, M., Praditia, T., Leiteritz, R., MacKinlay, D., Alesiani, F., Pflüger, D., Niepert, M.: PDEBench: An Extensive Benchmark for Scientific Machine Learning. In: 36th Conference on Neural Information Processing Systems (NeurIPS 2022) Track on Datasets and Benchmarks (2022).
  6. Leiteritz, R., Hurler, M., Pflüger, D.: Learning Free-Surface Flow with Physics-Informed Neural Networks. In: 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA). pp. 1664–1669 (2021). https://doi.org/10.1109/ICMLA52953.2021.00266.
  7. Leiteritz, R., Pflüger, D.: How to Avoid Trivial Solutions in Physics-Informed Neural Networks, https://arxiv.org/abs/2112.05620, (2021).
  8. Leiteritz, R., Hurler, M., Pflüger, D.: Learning Free-Surface Flow with Physics-Informed Neural Networks, http://arxiv.org/abs/2111.09705, (2021).
Zum Seitenanfang