Contact
+49 711 685 88447
+49 711 685 70413
Email
Universitätsstraße 38
70569 Stuttgart
Deutschland
Room: 2.105
Office Hours
on appointment
- Davis, K., Leiteritz, R., Pflüger, D., Schulte, M.: Deep learning based surrogate modeling for thermal plume prediction of groundwater heat pumps, (2023). https://doi.org/10.48550/arXiv.2302.08199.
- Takamoto, M., Praditia, T., Leiteritz, R., MacKinlay, D., Alesiani, F., Pflüger, D., Niepert, M.: PDEBench Datasets : Data for “PDEBench: An Extensive Benchmark for Scientific Machine Learning,” (2022). https://doi.org/10.18419/darus-2986.
- 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).
- Takamoto, M., Praditia, T., Leiteritz, R., MacKinlay, D., Alesiani, F., Pflüger, D., Niepert, M.: PDEBench Pretrained Models : Pretrained models for “PDEBench: An Extensive Benchmark for Scientific Machine Learning,” (2022). https://doi.org/10.18419/darus-2987.
- Leiteritz, R., Davis, K., Schulte, M., Pflüger, D.: A Deep Learning Approach for Thermal Plume Prediction of Groundwater Heat Pumps, (2022). https://doi.org/10.48550/arXiv.2203.14961.
- 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. pp. 1–8. Septentrio Academic Publishing (2022). https://doi.org/10.7557/18.6268.
- 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. 1668–1673. IEEE (2021). https://doi.org/10.1109/ICMLA52953.2021.00266.