DaRUS

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This is the Data Repository of the University of Stuttgart.

SFB 1313 "Interface-Driven Multi-Field Processes in Porous Media"

SFB 1333 "Molecular heterogeneous catalysis in confined geometries"

Institute of Biochemistry

Institute of Applied Mechanics (MIB)

Institute of Thermodynamics and Thermal Process Engineering

Stuttgart Center for Simulation Science (SC SimTech)

Cluster of Excellence Integrative Computational Design and Construction for Architecture (EXC IntCDC)

SFB/Transregio 161 "Quantitative Methods for Visual Computing"

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127,521 to 127,530 of 128,245 Results

plotBatchCoalLateAmendment.py Jul 31, 2020 - Replication data for "Importance of Specific Substrate Utilization by Microbes in Microbially Enhanced Coal-Bed Methane Production: A Modelling Study" Python Source Code - 12.7 KB - MD5: ed44dd3e200cd8019166835d3b25ca23 This produces the plot for a coal system with up to two amendment additions starting on day 76.
plotBatchCoal.py Jul 31, 2020 - Replication data for "Importance of Specific Substrate Utilization by Microbes in Microbially Enhanced Coal-Bed Methane Production: A Modelling Study" Python Source Code - 12.1 KB - MD5: 826e48d7bd300b86fcfb22b368ac3a3b This produces the plot for a coal system with up to three amendment additions (from day 0 on)
plotBatchGB.py Jul 31, 2020 - Replication data for "Importance of Specific Substrate Utilization by Microbes in Microbially Enhanced Coal-Bed Methane Production: A Modelling Study" Python Source Code - 12.0 KB - MD5: 24096d9a676972bf0b29826aa104259f This produces the plot for a glass beads system with up to three amendment additions.
Trained ANN Parameters for Physics-inspired Artificial Neural Network for Dynamic System 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.
ANN_params_MSE_L2_PHY.xlsx Jul 21, 2020 - Trained ANN Parameters for Physics-inspired Artificial Neural Network for Dynamic System MS Excel Spreadsheet - 19.1 KB - MD5: 9b88868eeef277937d0c729615e217de ANN parameters for the regularization method "MSE+L2+PHY"
ANN_params_MSE_L2.xlsx Jul 21, 2020 - Trained ANN Parameters for Physics-inspired Artificial Neural Network for Dynamic System MS Excel Spreadsheet - 18.7 KB - MD5: 9a85fe1d359cb162a8c4bc183545ce46 ANN parameters for the regularization method "MSE+L2"
ANN_params_MSE_PHY.xlsx Jul 21, 2020 - Trained ANN Parameters for Physics-inspired Artificial Neural Network for Dynamic System MS Excel Spreadsheet - 19.0 KB - MD5: 1b13a37e8fb1bf8ee2630519982ae0e4 ANN parameters for the regularization method "MSE+PHY"
ANN_params_MSE.xlsx Jul 21, 2020 - Trained ANN Parameters for Physics-inspired Artificial Neural Network for Dynamic System MS Excel Spreadsheet - 18.5 KB - MD5: ed241c309250b1d17eb682b750fa1244 ANN parameters for the regularization method "MSE"
README.md Jul 21, 2020 - Trained ANN Parameters for Physics-inspired Artificial Neural Network for Dynamic System Markdown Text - 3.0 KB - MD5: 3aa442599a974dcf6b70ad2c45da506c README file explaining how the data is formatted
Input-Output Dataset for Physics-inspired Artificial Neural Network for Dynamic 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 necessary for training, validating, and testing the ANN. The input data...

plotBatchCoalLateAmendment.py

Jul 31, 2020 - Replication data for "Importance of Specific Substrate Utilization by Microbes in Microbially Enhanced Coal-Bed Methane Production: A Modelling Study"

Python Source Code - 12.7 KB -

This produces the plot for a coal system with up to two amendment additions starting on day 76.

plotBatchCoal.py

Jul 31, 2020 - Replication data for "Importance of Specific Substrate Utilization by Microbes in Microbially Enhanced Coal-Bed Methane Production: A Modelling Study"

Python Source Code - 12.1 KB -

This produces the plot for a coal system with up to three amendment additions (from day 0 on)

plotBatchGB.py

Jul 31, 2020 - Replication data for "Importance of Specific Substrate Utilization by Microbes in Microbially Enhanced Coal-Bed Methane Production: A Modelling Study"

Python Source Code - 12.0 KB -

This produces the plot for a glass beads system with up to three amendment additions.

Trained ANN Parameters for Physics-inspired Artificial Neural Network for Dynamic System

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.

ANN_params_MSE_L2_PHY.xlsx

Jul 21, 2020 - Trained ANN Parameters for Physics-inspired Artificial Neural Network for Dynamic System

MS Excel Spreadsheet - 19.1 KB -

ANN parameters for the regularization method "MSE+L2+PHY"

ANN_params_MSE_L2.xlsx

Jul 21, 2020 - Trained ANN Parameters for Physics-inspired Artificial Neural Network for Dynamic System

MS Excel Spreadsheet - 18.7 KB -

ANN parameters for the regularization method "MSE+L2"

ANN_params_MSE_PHY.xlsx

Jul 21, 2020 - Trained ANN Parameters for Physics-inspired Artificial Neural Network for Dynamic System

MS Excel Spreadsheet - 19.0 KB -

ANN parameters for the regularization method "MSE+PHY"

ANN_params_MSE.xlsx

Jul 21, 2020 - Trained ANN Parameters for Physics-inspired Artificial Neural Network for Dynamic System

MS Excel Spreadsheet - 18.5 KB -

ANN parameters for the regularization method "MSE"

README.md

Jul 21, 2020 - Trained ANN Parameters for Physics-inspired Artificial Neural Network for Dynamic System

Markdown Text - 3.0 KB -

README file explaining how the data is formatted

Input-Output Dataset for Physics-inspired Artificial Neural Network for Dynamic 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 necessary for training, validating, and testing the ANN. The input data...

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