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Learning Groundwater Contaminant Diffusion-Sorption Processes With a Finite Volume Neural Network

, , , , , and . Water resources research, 58 (12): e2022WR033149 (2022)
DOI: 10.1029/2022WR033149

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Prognosis of water levels in a moor groundwater system influenced by hydrology and water extraction using an artificial neural network, , , , , and . EGU General Assembly 2022, page 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.Finite Volume Neural Networks: a Hybrid Modeling Strategy for Subsurface Contaminant Transport, , and . (2021)Using physics-based regularization in Artificial Neural Networks to predict thermochemical energy storage systems, , , and . American Geophysical Union, Fall Meeting 2019, page IN32B-15. Smithsonian Astrophysical Observatory, (2019)Universal Differential Equation for Diffusion-Sorption Problem in Porous Media Flow, , and . EGU General Assembly 2022, page EGU21-49. Copernicus GmbH, (2021)The Deep Arbitrary Polynomial Chaos Neural Network or how Deep Artificial Neural Networks could benefit from Data-Driven Homogeneous Chaos Theory, , , , , and . Neural Networks, (2023 (accepted))PDEBench: An Extensive Benchmark for Scientific Machine Learning, , , , , , and . 36th Conference on Neural Information Processing Systems (NeurIPS 2022) Track on Datasets and Benchmarks, (2022)Improving Thermochemical Energy Storage Dynamics Forecast with Physics-Inspired Neural Network Architecture, , , and . Energies, 13 (15): 3873 (2020)PDEBench Datasets, , , , , , and . (2022)Physics-Informed Machine Learning in Hydrology II Oral, , and . American Geophysical Union, Fall Meeting 2021, page H34F. American Geophysical Union, (2021)