doc

Author: dario-coscia

docs/source/_tutorial.rst

@@ -1,35 +1,43 @@
-PINA Tutorials
-======================
+🚀 Welcome to the PINA Tutorials!
+==================================
 
 
-In this folder we collect useful tutorials in order to understand the principles and the potential of **PINA**.
+In this folder we collect useful tutorials in order to understand the principles and the potential of **PINA**. 
+Whether you're just getting started or looking to deepen your understanding, these resources are here to guide you.
 
 Getting started with PINA
 -------------------------
 
-- `Introduction to PINA for Physics Informed Neural Networks training <tutorial1/tutorial.html>`_
+- `Introductory Tutorial: A Beginner's Guide to PINA <tutorial17/tutorial.html>`_
+- `How to build a Problem in PINA <tutorial16/tutorial.html>`_
+- `Introduction to Solver classes <tutorial18/tutorial.html>`_
+- `Introduction to Trainer class <tutorial11/tutorial.html>`_
+- `Data structure for SciML: Tensor, LabelTensor, Data and Graph <tutorial19/tutorial.html>`_
+- `Building geometries with DomainInterface class <tutorial6/tutorial.html>`_
 - `Introduction to PINA Equation class <tutorial12/tutorial.html>`_
-- `PINA and PyTorch Lightning, training tips and visualizations <tutorial11/tutorial.html>`_
-- `Building custom geometries with PINA Location class <tutorial6/tutorial.html>`_
-
 
 Physics Informed Neural Networks
 --------------------------------
 
-- `Two dimensional Poisson problem using Extra Features Learning <tutorial2/tutorial.html>`_
-- `Two dimensional Wave problem with hard constraint <tutorial3/tutorial.html>`_
-- `Resolution of a 2D Poisson inverse problem <tutorial7/tutorial.html>`_
-- `Periodic Boundary Conditions for Helmotz Equation <tutorial9/tutorial.html>`_
-- `Multiscale PDE learning with Fourier Feature Network <tutorial13/tutorial.html>`_
+- `Introductory Tutorial: Physics Informed Neural Networks with PINA <tutorial1/tutorial.html>`_
+- `Enhancing PINNs with Extra Features to solve the Poisson Problem <tutorial2/tutorial.html>`_
+- `Applying Hard Constraints in PINNs to solve the Wave Problem <tutorial3/tutorial.html>`_
+- `Applying Periodic Boundary Conditions in PINNs to solve the Helmotz Problem <tutorial9/tutorial.html>`_
+- `Inverse Problem Solving with Physics-Informed Neural Network <tutorial7/tutorial.html>`_
+- `Learning Multiscale PDEs Using Fourier Feature Networks <tutorial13/tutorial.html>`_
+- `Learning Bifurcating PDE Solutions with Physics-Informed Deep Ensembles <tutorial14/tutorial.html>`_
 
 Neural Operator Learning
 ------------------------
 
-- `Two dimensional Darcy flow using the Fourier Neural Operator <tutorial5/tutorial.html>`_
-- `Time dependent Kuramoto Sivashinsky equation using the Averaging Neural Operator <tutorial10/tutorial.html>`_
+- `Introductory Tutorial: Neural Operator Learning with PINA <tutorial21/tutorial.html>`_
+- `Modeling 2D Darcy Flow with the Fourier Neural Operator <tutorial5/tutorial.html>`_
+- `Solving the Kuramoto-Sivashinsky Equation with Averaging Neural Operator <tutorial10/tutorial.html>`_
 
 Supervised Learning
 -------------------
 
-- `Unstructured convolutional autoencoder via continuous convolution <tutorial4/tutorial.html>`_
-- `POD-RBF and POD-NN for reduced order modeling <tutorial8/tutorial.html>`_
+- `Introductory Tutorial: Supervised Learning with PINA  <tutorial20/tutorial.html>`_
+- `Chemical Properties Prediction with Graph Neural Networks <tutorial25/tutorial.html>`_
+- `Unstructured Convolutional Autoencoders with Continuous Convolution <tutorial4/tutorial.html>`_
+- `Reduced Order Modeling with POD-RBF and POD-NN Approaches for Fluid Dynamics <tutorial8/tutorial.html>`_