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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 Transformative Architecture (EXC IntCDC)

SFB/Transregio 161 "Quantitative Methods for Visual Computing"

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1 to 10 of 150,479 Results

combine_xyz_models_add_ref.py May 26, 2026 - Data for: PerSiVal: deep neural networks for pervasive simulation of an activation-driven continuum-mechanical upper limb model Python Source Code - 2.9 KB - MD5: 33b3ad607faa81f028ea2dc7ce76e0be Script to combine the three separately trained networks for the coordinate components into one, adding the reference coordinates to the bias of the output layer in the process, to convert from deformation to Cartesian coordinates.
cross_validation.py May 26, 2026 - Data for: PerSiVal: deep neural networks for pervasive simulation of an activation-driven continuum-mechanical upper limb model Python Source Code - 1.3 KB - MD5: 20d942d0689d903eb57295120b8912a5 Utility script containing all the code needed for some basic cross validation training.
eval_sparse_grid.py May 26, 2026 - Data for: PerSiVal: deep neural networks for pervasive simulation of an activation-driven continuum-mechanical upper limb model Python Source Code - 3.5 KB - MD5: 0a47f1396b6027c593a257941b3b6439 Script file containing all the code needed to evaluate the sparse grid surrogate based on the data provided in this dataset.
saved_model.pb May 26, 2026 - Data for: PerSiVal: deep neural networks for pervasive simulation of an activation-driven continuum-mechanical upper limb model Unknown - 337.3 KB - MD5: de2ed2b6631bd880c8e473158e2f8060 Content of the neural network model predicting muscle surface coordinates based on muscle activation. Needs to be inside a folder representing the entire TensorFlow model.
sg_angle_data.npy May 26, 2026 - Data for: PerSiVal: deep neural networks for pervasive simulation of an activation-driven continuum-mechanical upper limb model Unknown - 140.4 KB - MD5: 2c6ff93e346677b9ea1955e8f3ef2aa6 Data representing a sparse grid surrogate, taking activation levels as input and outputting the corresponding elbow joint angle from the FE model.
sg_eval_example_script.py May 26, 2026 - Data for: PerSiVal: deep neural networks for pervasive simulation of an activation-driven continuum-mechanical upper limb model Python Source Code - 1.4 KB - MD5: 9147e8480bf4b3163bee6f6bdc4442dd Script explaining how to evaluate the sparse grid surrogate based on the data provided in this dataset.
sg_surf_coords_biceps.npy May 26, 2026 - Data for: PerSiVal: deep neural networks for pervasive simulation of an activation-driven continuum-mechanical upper limb model Unknown - 135.5 MB - MD5: 726bfd368a99ac024441e4a3cb27720b Pickled Numpy-file containing the support for the sparse grid interpolation of the Cartesian coordinates of each finite element mesh-node on the surface of the biceps'.
sg_surf_coords_biceps_tr_data.npy May 26, 2026 - Data for: PerSiVal: deep neural networks for pervasive simulation of an activation-driven continuum-mechanical upper limb model Unknown - 112.1 MB - MD5: 22dd49433006aa98f56eae5cc78d79b4 Numpy array containing the training data for the neural network model (net_acts2coords_biceps) as input-output sample pairs. Each line contains muscle activation input in the first five columns, followed by 2809 x coordinates, then just as many y coordinates, then z coordinates.
test_random_activations.py May 26, 2026 - Data for: PerSiVal: deep neural networks for pervasive simulation of an activation-driven continuum-mechanical upper limb model Python Source Code - 2.1 KB - MD5: 880e0051ff4bc3d8f2d017446c394ad0 Script to test the combined neural network model for the worst case error on randomly picked activation vectors.
test_random_angles.py May 26, 2026 - Data for: PerSiVal: deep neural networks for pervasive simulation of an activation-driven continuum-mechanical upper limb model Python Source Code - 4.2 KB - MD5: 91b4ff6285a8424dcea080a3e633311a Script to test the combined neural network model for the worst case error on randomly picked joint angles. For each angle an optimization is run using the sparse grid surrogate to determine optimal activation levels for it.

combine_xyz_models_add_ref.py

May 26, 2026 - Data for: PerSiVal: deep neural networks for pervasive simulation of an activation-driven continuum-mechanical upper limb model

Python Source Code - 2.9 KB -

Script to combine the three separately trained networks for the coordinate components into one, adding the reference coordinates to the bias of the output layer in the process, to convert from deformation to Cartesian coordinates.

cross_validation.py

May 26, 2026 - Data for: PerSiVal: deep neural networks for pervasive simulation of an activation-driven continuum-mechanical upper limb model

Python Source Code - 1.3 KB -

Utility script containing all the code needed for some basic cross validation training.

eval_sparse_grid.py

May 26, 2026 - Data for: PerSiVal: deep neural networks for pervasive simulation of an activation-driven continuum-mechanical upper limb model

Python Source Code - 3.5 KB -

Script file containing all the code needed to evaluate the sparse grid surrogate based on the data provided in this dataset.

saved_model.pb

May 26, 2026 - Data for: PerSiVal: deep neural networks for pervasive simulation of an activation-driven continuum-mechanical upper limb model

Unknown - 337.3 KB -

Content of the neural network model predicting muscle surface coordinates based on muscle activation. Needs to be inside a folder representing the entire TensorFlow model.

sg_angle_data.npy

May 26, 2026 - Data for: PerSiVal: deep neural networks for pervasive simulation of an activation-driven continuum-mechanical upper limb model

Unknown - 140.4 KB -

Data representing a sparse grid surrogate, taking activation levels as input and outputting the corresponding elbow joint angle from the FE model.

sg_eval_example_script.py

May 26, 2026 - Data for: PerSiVal: deep neural networks for pervasive simulation of an activation-driven continuum-mechanical upper limb model

Python Source Code - 1.4 KB -

Script explaining how to evaluate the sparse grid surrogate based on the data provided in this dataset.

sg_surf_coords_biceps.npy

May 26, 2026 - Data for: PerSiVal: deep neural networks for pervasive simulation of an activation-driven continuum-mechanical upper limb model

Unknown - 135.5 MB -

Pickled Numpy-file containing the support for the sparse grid interpolation of the Cartesian coordinates of each finite element mesh-node on the surface of the biceps'.

sg_surf_coords_biceps_tr_data.npy

May 26, 2026 - Data for: PerSiVal: deep neural networks for pervasive simulation of an activation-driven continuum-mechanical upper limb model

Unknown - 112.1 MB -

Numpy array containing the training data for the neural network model (net_acts2coords_biceps) as input-output sample pairs. Each line contains muscle activation input in the first five columns, followed by 2809 x coordinates, then just as many y coordinates, then z coordinates.

test_random_activations.py

May 26, 2026 - Data for: PerSiVal: deep neural networks for pervasive simulation of an activation-driven continuum-mechanical upper limb model

Python Source Code - 2.1 KB -

Script to test the combined neural network model for the worst case error on randomly picked activation vectors.

test_random_angles.py

May 26, 2026 - Data for: PerSiVal: deep neural networks for pervasive simulation of an activation-driven continuum-mechanical upper limb model

Python Source Code - 4.2 KB -

Script to test the combined neural network model for the worst case error on randomly picked joint angles. For each angle an optimization is run using the sparse grid surrogate to determine optimal activation levels for it.

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