Institute for Modelling and Simulation of Biomechanical Systems

Our vision at the Institute for Modelling and Simulation of Biomechanical Systems (IMSB) is to use computational and experimental methods to enhance our knowledge about biomechanical systems with a particular focus on the Human System. For more information please also visit our institute's website.

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2,461 to 2,470 of 3,663 Results

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.
train_xyz_nets_separately.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 - 4.0 KB - MD5: 4af90871e7e385b13a5ab2e646178b07 Script to setup and train three neural networks, one for each coordinate dimension. Will save the models into a models subfolder.
variables.data-00000-of-00001 May 26, 2026 - Data for: PerSiVal: deep neural networks for pervasive simulation of an activation-driven continuum-mechanical upper limb model Unknown - 167.4 MB - MD5: d1c555ea094780eb6600f2d33dc7a56e 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, in a subfolder called "variables".
variables.index May 26, 2026 - Data for: PerSiVal: deep neural networks for pervasive simulation of an activation-driven continuum-mechanical upper limb model Unknown - 2.7 KB - MD5: 4d3d19a284e14c0f0c8d172ca026bbad 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, inside a subfolder called "variables".
Supplementary material for "Neuromusculoskeletal Modeling of Human Bipedal Gaits: Exploring the Role of Reflexes in Locomotion" May 22, 2026 - Walking Model Bunz, Elsa, 2026, "Supplementary material for "Neuromusculoskeletal Modeling of Human Bipedal Gaits: Exploring the Role of Reflexes in Locomotion"", https://doi.org/10.18419/DARUS-5066, DaRUS, V1 This dataset contains the supplementary electronic material to the PhD thesis Neuromusculoskeletal Modeling of Human Bipedal Gaits: Exploring the Role of Reflexes in Locomotion (E.K. Bunz, 2025, doi: 10.18419/opus-18571). Always cite the thesis together with this dataset or refer to the corresponding publications.
config_multRamps.scone May 22, 2026 - Supplementary material for "Neuromusculoskeletal Modeling of Human Bipedal Gaits: Exploring the Role of Reflexes in Locomotion" Unknown - 1.4 KB - MD5: b3f2e0c44ee5b5cdd7658c66aefafe6f
config_multRamps.scone May 22, 2026 - Supplementary material for "Neuromusculoskeletal Modeling of Human Bipedal Gaits: Exploring the Role of Reflexes in Locomotion" Unknown - 1.3 KB - MD5: cd9c439f9163dde4b9bf2e576f2da110
config_steps.scone May 22, 2026 - Supplementary material for "Neuromusculoskeletal Modeling of Human Bipedal Gaits: Exploring the Role of Reflexes in Locomotion" Unknown - 939 B - MD5: 734f82242a45d9ea14224775ea549c58
config_steps.scone May 22, 2026 - Supplementary material for "Neuromusculoskeletal Modeling of Human Bipedal Gaits: Exploring the Role of Reflexes in Locomotion" Unknown - 953 B - MD5: 184906e5e4de93f8752c703392eece3e

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.

train_xyz_nets_separately.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 - 4.0 KB -

Script to setup and train three neural networks, one for each coordinate dimension. Will save the models into a models subfolder.

variables.data-00000-of-00001

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

Unknown - 167.4 MB -

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, in a subfolder called "variables".

variables.index

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

Unknown - 2.7 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, inside a subfolder called "variables".

Supplementary material for "Neuromusculoskeletal Modeling of Human Bipedal Gaits: Exploring the Role of Reflexes in Locomotion"

May 22, 2026 - Walking Model

Bunz, Elsa, 2026, "Supplementary material for "Neuromusculoskeletal Modeling of Human Bipedal Gaits: Exploring the Role of Reflexes in Locomotion"", https://doi.org/10.18419/DARUS-5066, DaRUS, V1

This dataset contains the supplementary electronic material to the PhD thesis Neuromusculoskeletal Modeling of Human Bipedal Gaits: Exploring the Role of Reflexes in Locomotion (E.K. Bunz, 2025, doi: 10.18419/opus-18571). Always cite the thesis together with this dataset or refer to the corresponding publications.

config_multRamps.scone

May 22, 2026 - Supplementary material for "Neuromusculoskeletal Modeling of Human Bipedal Gaits: Exploring the Role of Reflexes in Locomotion"

Unknown - 1.4 KB -

config_multRamps.scone

May 22, 2026 - Supplementary material for "Neuromusculoskeletal Modeling of Human Bipedal Gaits: Exploring the Role of Reflexes in Locomotion"

Unknown - 1.3 KB -

config_steps.scone

May 22, 2026 - Supplementary material for "Neuromusculoskeletal Modeling of Human Bipedal Gaits: Exploring the Role of Reflexes in Locomotion"

Unknown - 939 B -

config_steps.scone

May 22, 2026 - Supplementary material for "Neuromusculoskeletal Modeling of Human Bipedal Gaits: Exploring the Role of Reflexes in Locomotion"

Unknown - 953 B -

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