We prepared four tutorials for you that explain the structure of our library and the intended way to use it.
1. Problems and solutions
In this tutorial you learn how to
- set up custom problems
- define solutions to them
- work with trivial solutions
- plot problems and solutions
- compute the displacements and von Mises stresses for solutions and how to plot them
2. Datasets and dataloaders
In this tutorial you learn how to
- load datasets, e.g., the SELTO datasets
- how to use pyvista plotting
- work with datasets
- get dataloaders from datasets
- create custom datasets
3. Topo solvers
In this tutorial you learn how to
- use optimization criteria as objective functions or as evaluation metrics
- use our topo solvers, particularly our SIMP topo solver
4. Trainable topo solvers
In this tutorial you learn how to
- use different data preprocessings
- use neural networks for topology optimization
- train and evaluate different models
- use our UNets
- apply equivariance to a neural network