Published December 2024 | Version v3
Dataset Open

Gaussian Process Regression + Deep Neural Network Autoencoder for Probabilistic Surrogate Modeling in Nonlinear Mechanics of Solids [Supplementary data]

Description

Complementary data for : https://arxiv.org/abs/2407.10732

Repository : https://github.com/saurabhdeshpande93/gp-auto-regression

data
     |--- README.md
     |--- .gitignore
     |--- pretrained_models
     |              |--- best.h5 (optimised autoencoder weights)
     |              |--- best_GPs 
     |                      |
     |                      |- gp_{i}.pkl (optimised GP models)
     |
     |--- training_data
                    |
                    |--- *.npy (numpy arrays of train/test data)

 

Files

data.zip

Files (4.7 GB)

Name Size
md5:5e768a40c1021e37f4fee72d226c6545
4.7 GB Preview Download

Additional details

Related works

Is supplement to
Preprint: arXiv:2407.10732 (arXiv)

Funding

European Commission
RAINBOW - Rapid Biomechanics Simulation for Personalized Clinical Design 764644
European Commission
MOrPhEM - Mechanics of Programmable Matter 800150

Software

Repository URL
https://github.com/saurabhdeshpande93/gp-auto-regression
Programming language
Python
Development Status
Active