Mesh predictor

Tensorflow and optuna-based utility to learn to predict several quantities (position, deviation, thickness) based on a set of process parameters of FEM experiments.

The documentation is at https://hamkerlab.github.io/ML-Karoprod-MeshPredictor/.

Installation

Dependencies:

• numpy
• pandas
• matplotlib
• ipywidgets
• tensorflow >=2.6
• optuna
• h5py

pip install git+https://github.com/hamkerlab/ML-Karoprod-MeshPredictor.git@master

Documentation

To generate the documentation, you will need:

pip install mkdocs mkdocs-material mkdocstrings pymdown-extensions mknotebooks

To see the documentation locally:

mkdocs serve

To push to github:

mkdocs gh-deploy