Bibliograph. Daten | Hirche, Manuel: Application and extension of a super resolution physics-informed convolutional neural network to groundwater modelling. Universität Stuttgart, Fakultät Informatik, Elektrotechnik und Informationstechnik, Bachelorarbeit Nr. 23 (2023). 44 Seiten, englisch.
|
Kurzfassung | The computational effort of a simulation can be reduced by running simulations on a coarse grid and interpolating to a fine one. This interpolation can be done using data-driven neural networks, called super-resolution. To minimize the need to perform expensive simulations to create the datasets required for training, physics-informed neural networks (PINNs) add a physical error term to the learning process. In this work, we extend and apply a super-resolution PINN approach to a groundwater simulation with heat pumps. We extend an existing network model for flow velocity and pressure to include temperature and permeability of the soil and derive a corresponding error term. The model is trained on a data set from a simulation with different pressures and permeabilities. The results are compared with a data-driven network and bicubic interpolation. We find that both neural networks significantly outperform bicubic interpolation, whereas the PINN approach achieves slightly better results than the data-driven network.
|
Volltext und andere Links | Volltext
|
Abteilung(en) | Universität Stuttgart, Institut für Parallele und Verteilte Systeme, Simulation großer Systeme
|
Betreuer | Schulte, Prof. Miriam; Pelzer, Julia |
Eingabedatum | 15. September 2023 |
---|