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Prognosis of water levels in a moor groundwater system influenced by hydrology and water extraction using an artificial neural network, , , , , und . EGU General Assembly 2022, Seite EGU21-3013. Copernicus GmbH, (2021)Input-Output Dataset for Physics-inspired Artificial Neural Network for Dynamic System. Dataset, (2020)Related to: Praditia, T., Walser, T., Oladyshkin, S. and Nowak, W.: Improving Thermochemical Energy Storage dynamics forecast with Physics-Inspired Neural Network architecture. Energies 2020.Universal Differential Equation for Diffusion-Sorption Problem in Porous Media Flow, , und . EGU General Assembly 2022, Seite EGU21-49. Copernicus GmbH, (2021)Using physics-based regularization in Artificial Neural Networks to predict thermochemical energy storage systems, , , und . American Geophysical Union, Fall Meeting 2019, Seite IN32B-15. Smithsonian Astrophysical Observatory, (2019)The Deep Arbitrary Polynomial Chaos Neural Network or how Deep Artificial Neural Networks could benefit from Data-Driven Homogeneous Chaos Theory, , , , , und . Neural Networks, (2023 (accepted))Finite Volume Neural Networks: a Hybrid Modeling Strategy for Subsurface Contaminant Transport, , und . (2021)PDEBench: An Extensive Benchmark for Scientific Machine Learning, , , , , , und . 36th Conference on Neural Information Processing Systems (NeurIPS 2022) Track on Datasets and Benchmarks, (2022)Learning Groundwater Contaminant Diffusion-Sorption Processes With a Finite Volume Neural Network, , , , , und . Water resources research, 58 (12): e2022WR033149 (2022)PDEBench Pretrained Models : Pretrained models for "PDEBench: An Extensive Benchmark for Scientific Machine Learning", , , , , , und . 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.Infering Boundary Conditions in Finite Volume Neural Networks, , , , , , und . Artificial Neural Networks and Machine Learning : ICANN 2022, Volume 1 von Lecture Notes in Computer Science, Seite 538-549. Cham, Springer, (2022)