In this folder we collect useful tutorials in order to understand the principles and the potential of PINA. Please read the following table for details about the tutorials. The HTML version of all the tutorials is available also within the documentation.
| Description | Tutorial |
|---|---|
| Introductory Tutorial: A Beginner’s Guide to PINA | [.ipynb, .py, .html] |
How to build a Problem in PINA |
[.ipynb, .py, .html] |
| Introduction to Solver classes | [.ipynb, .py, .html] |
Introduction to Trainer class |
[.ipynb, .py, .html] |
Data structure for SciML: Tensor, LabelTensor, Data and Graph |
[.ipynb, .py, .html] |
Building geometries with DomainInterface class |
[.ipynb, .py, .html] |
Introduction to PINA Equation class |
[.ipynb, .py, .html] |
| Description | Tutorial |
|---|---|
| Introduction to Physics Informed Neural Networks training | [.ipynb, .py, .html] |
| Two dimensional Poisson problem using Extra Features Learning | [.ipynb, .py, .html] |
| Two dimensional Wave problem with hard constraint | [.ipynb, .py, .html] |
| Resolution of a 2D Poisson inverse problem | [.ipynb, .py, .html] |
| Periodic Boundary Conditions for Helmotz Equation | [.ipynb, .py, .html] |
| Multiscale PDE learning with Fourier Feature Network | [.ipynb, .py, .html] |
| Description | Tutorial |
|---|---|
| Two dimensional Darcy flow using the Fourier Neural Operator | [.ipynb, .py, .html] |
| Time dependent Kuramoto Sivashinsky equation using the Averaging Neural Operator | [.ipynb, .py, .html] |
| Description | Tutorial |
|---|---|
| Unstructured convolutional autoencoder via continuous convolution | [.ipynb, .py, .html] |
| POD-RBF and POD-NN for reduced order modeling | [.ipynb, .py, .html] |
| POD-RBF for modelling Lid Cavity | [.ipynb, .py, .html] |