T. Praditia, M. Karlbauer, S. Otte, S. Oladyshkin, M. Butz, und W. Nowak. Dataset, (2022)Related to: Praditia, T., Karlbauer, M., Otte, S., Oladyshkin, S., Butz, M.V., Nowak, W.: Learning Groundwater Contaminant Diffusion-Sorption Processes with a Finite Volume Neural Network. Earth and Space Science Open Archive (2022). doi: 10.1002/essoar.10511934.1.
M. Takamoto, T. Praditia, R. Leiteritz, D. MacKinlay, F. Alesiani, D. Pflüger, und 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.
L. Kloker, und C. Bringedal. Dataset, (2022)Related to: Leon H. Kloker and Carina Bringedal, Solution approaches for evaporation-driven density instabilities in a slab of saturated porous media, Physics of Fluids 34, 096606 (2022). doi: 10.1063/5.0110129.
M. Takamoto, T. Praditia, R. Leiteritz, D. MacKinlay, F. Alesiani, D. Pflüger, und 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.
C. Bringedal, G. Pieters, und C. Duijn. Dataset, (2022)Related to: C. Bringedal, T. Schollenberger, G. J. M. Pieters, C. J. van Duijn, R. Helmig, Evaporation-driven density instabilities in saturated porous media. Transport in Porous Media. 2022. doi: 10.1007/s11242-022-01772-w.
I. Banerjee. Dataset, (2021)Related to: Banerjee, I., Guthke, A., Van de Ven, C. J. C., Mumford, K. G. & Nowak, W. (2021). Overcoming the Model-Data-Fit Problem in Porous Media: A Quantitative Method to Compare Invasion-Percolation Models to High-Resolution Data. Water Resources Research, 57, e2021WR029986. doi: 10.1029/2021WR029986.
A. Gonzalez-Nicolas Alvarez. Dataset, (2021)Related to: Gonzalez-Nicolas, A.; Schwientek, M.; Sinsbeck, M; Nowak, W. Characterization of export regimes in concentration-discharge plots via an advanced time-series model and event-based sampling strategies. Water 2021, 13, 1723. doi: 10.3390/w13131723.
T. Praditia. 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.
T. Praditia. 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.
S. Schulz, C. Bringedal, und S. Ackermann. Dataset, (2021)Related to: SimTech Project work "Herleitung reduzierter Modelle einer Zweiphasenströmung zwischen parallelen Platten mit Slip-Bedingungen".
H. Hsueh. Dataset, (2021)Related to: Han-Fang Hsueh, Anneli Guthke, Thomas Wöhling, Wolfgang Nowak: Diagnosis of model-structural errors with a sliding time-window Bayesian analysis. In: Water Resource Research (submitted). arXiv: 2107.09399.
A. Gonzalez-Nicolas Alvarez. Software, (2021)Related to: Gonzalez-Nicolas, A.; Schwientek, M.; Sinsbeck, M; Nowak, W. Characterization of export regimes in concentration-discharge plots via an advanced time-series model and event-based sampling strategies. Water 2021, 13, 1723. doi: 10.3390/w13131723.
L. Scholz, und C. Bringedal. Dataset, (2021)Related to: Scholz, L., Bringedal, C. A Three-Dimensional Homogenization Approach for Effective Heat Transport in Thin Porous Media. Transp Porous Med (2022). doi: 10.1007/s11242-022-01746-y.
C. Bringedal. Dataset, (2021)Related to: Tufan Ghosh, Carina Bringedal, Rainer Helmig, G.P. Raja Sekhar, Upscaled equations for two-phase flow in highly heterogeneous porous media: Varying permeability and porosity, Advances in Water Resources 145, 2020, 103716. doi: 10.1016/j.advwatres.2020.103716.
S. Reuschen, T. Xu, und W. Nowak. Dataset, (2020)Related to: Reuschen, S., Xu, T., Nowak, W., 2020. Bayesian inversion of hierarchical geostatistical models using a parallel-tempering sequential Gibbs MCMC. Advances in Water Resources 141, 103614. doi: 10.1016/j.advwatres.2020.103614.
