1 to 10 of 30 Results
Sep 6, 2019
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Sep 6, 2019EXC 2075 Project Networks
SimTech EXC 2075 Project Network 5 "On-the-fly model modification, error control, and simulation adaptivity" |
Sep 6, 2019EXC 2075 Project Networks
SimTech EXC 2075 Project Network 1 "Data-integrated models and methods for multiphase fluid dynamics" |
Feb 20, 2020PN 5
SimTech Project PN 5-6 "Physics-informed ANNs for dynamic, distributed and stochastic systems" |
Feb 20, 2020PN 5-6
This dataverse contains dataset and codes for the submitted publication: Praditia, T., Walser, T., Oladyshkin, S. and Nowak, W. (2020): Physics-inspired Artificial Neural Network structure improves prediction: Application to a Thermochemical Energy Storage System |
Jul 21, 2020 - PINN Dynamic System
Praditia, Timothy, 2020, "Input-Output Dataset for Physics-inspired Artificial Neural Network for Dynamic System", https://doi.org/10.18419/darus-633, DaRUS, V1
This dataset contains two .mat files, one pre-processed (direct simulation results) and the other one is with added noise. The simulated problem is a thermochemical energy storage problem using CaO/Ca(OH)2 as the material choice. This dataset is used as input-output data pairs ne... |
Jul 21, 2020 - PINN Dynamic System
Praditia, Timothy, 2020, "Trained ANN Parameters for Physics-inspired Artificial Neural Network for Dynamic System", https://doi.org/10.18419/darus-634, DaRUS, V1
This dataset contains four .xlsx files containing trained values of the ANN weights and biases, along with the hyperparameter values at the end of the training (with noisy dataset). These four files correspond to four different regularization methods. |
Mar 1, 2021
This dataverse hosts data(-verses) without a project network affiliation. |
Mar 23, 2022Projects without PN Affiliation
This is a benchmark suite of PDE simulations comprising data for testing the performance of novel simulation or emulation methods against classical simulations. |
Nov 2, 2022 - PN 5-6
Praditia, Timothy; Karlbauer, Matthias; Otte, Sebastian; Oladyshkin, Sergey; Butz, Martin V.; Nowak, Wolfgang, 2022, "Replication Data for: Learning Groundwater Contaminant Diffusion-Sorption Processes with a Finite Volume Neural Network", https://doi.org/10.18419/darus-3249, DaRUS, V1
This dataset contains diffusion-sorption data, generated with numerical simulation based on three different sorption isotherms, namely the linear, Freundlich, and Langmuir isotherms. This dataset is used to train, validate, and test all the deep learning models that are used in t... |