R. Leiteritz, M. Hurler, und D. Pflüger. 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA), Seite 1668-1673. Piscataway, IEEE, (2021)
J. Pelzer. Dataset, (2023)Related to: Pelzer, Julia, and Miriam Schulte. "Efficient two-stage modeling of heat plume interactions of geothermal heat pumps in shallow aquifers using convolutional neural networks." Geoenergy Science and Engineering (2024): 212788. doi: 10.1016/j.geoen.2024.212788.
J. Pelzer. Dataset, (2024)Related to: Pelzer, Julia, and Miriam Schulte. "Scalable Machine Learning Approach for Heat Transport in Groundwater". In proceedings of 23rd International Conference of Machine Learning and Applications (2024). In progress.
C. Homs Pons, and R. Lautenschlager. Software, (2024)Related to: Coupled Simulations and Parameter Inversion for Neural System and Electrophysiological Muscle Models, submitted to GAMM Mitteilungen.
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).
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.
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.
J. Pelzer. Dataset, (2023)Related to: Pelzer, Julia, and Miriam Schulte. "Efficient two-stage modeling of heat plume interactions of geothermal heat pumps in shallow aquifers using convolutional neural networks." Geoenergy Science and Engineering (2024): 212788. doi: 10.1016/j.geoen.2024.212788.
J. Pelzer. Dataset, (2023)Related to: Pelzer, Julia, and Miriam Schulte. "Efficient two-stage modeling of heat plume interactions of geothermal heat pumps in shallow aquifers using convolutional neural networks." Geoenergy Science and Engineering (2024): 212788. doi: 10.1016/j.geoen.2024.212788.
J. Pelzer. Software, (2024)Related to: Pelzer, Julia, and Miriam Schulte. "Efficient two-stage modeling of heat plume interactions of geothermal heat pumps in shallow aquifers using convolutional neural networks." Geoenergy Science and Engineering (2024): 212788. doi: 10.1016/j.geoen.2024.212788.
J. Pelzer. Software, (2024)Related to: Pelzer, Julia, and Miriam Schulte. "Efficient two-stage modeling of heat plume interactions of geothermal heat pumps in shallow aquifers using convolutional neural networks." Geoenergy Science and Engineering (2024): 212788. doi: 10.1016/j.geoen.2024.212788.
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.
J. Pelzer. Dataset, (2023)Related to: Pelzer, Julia, and Miriam Schulte. "Efficient two-stage modeling of heat plume interactions of geothermal heat pumps in shallow aquifers using convolutional neural networks." Geoenergy Science and Engineering (2024): 212788. doi: 10.1016/j.geoen.2024.212788.
J. Pelzer. Dataset, (2024)Related to: Pelzer, Julia, and Miriam Schulte. "Scalable Machine Learning Approach for Heat Transport in Groundwater". In proceedings of 23rd International Conference of Machine Learning and Applications (2024). In progress.
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.