Contact
+49 711 685 61505
+49 711 685 71505
Email
Universitätsstraße 38
70569 Stuttgart
Deutschland
Room: 2.414
Office Hours
on appointment
Subject
- Takamoto, M., Praditia, T., Leiteritz, R., MacKinlay, D., Alesiani, F., Pflüger, D., Niepert, M.: PDEBench Datasets, https://doi.org/10.18419/darus-2986, (2022). https://doi.org/10.18419/darus-2986.
- 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).
- 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.
- 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.
- 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).
- 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.
- Leiteritz, R., Pflüger, D.: How to Avoid Trivial Solutions in Physics-Informed Neural Networks, https://arxiv.org/abs/2112.05620, (2021).
- Leiteritz, R., Hurler, M., Pflüger, D.: Learning Free-Surface Flow with Physics-Informed Neural Networks, http://arxiv.org/abs/2111.09705, (2021).