Skip to main content
Ctrl+K
pyLOM pyLOM
  • API reference
  • Examples
  • Installation
  • GitHub
  • API reference
  • Examples
  • Installation
  • GitHub

Section Navigation

  • Example of parallel Proper Orthogonal Decomposition
  • SHRED at scale
  • Train a PINN (Physics Informed Neural Network) with a custom PDE
  • How to train an MLP using the Pipeline
  • How to train an KAN using the Pipeline
  • Examples

Examples#

This is the examples section, here you can find some notebooks that will guide you through the basic usage of pyLOM. The data used for these notebooks can be found in the following Hugging face repository: https://huggingface.co/datasets/bef-18/pyLOM_examples/tree/main

  • Example of parallel Proper Orthogonal Decomposition
  • SHRED at scale
    • Step 1: parallel POD and sensor interpolation
    • Step 2: Fit SHRED
    • Step 3: inference SHRED
    • Step 4: parallel reconstruction
  • Train a PINN (Physics Informed Neural Network) with a custom PDE
    • Define the collocation points
    • Define the PDE
    • Define the boundary conditions
    • Train the pinn
    • Make the predictions and plot the results
    • Save and load the model
  • How to train an MLP using the Pipeline
    • Import classes and define paths
    • Define scalers if needed
    • Create datasets
    • Model creation
    • Run the pipeline
    • Show plots
    • Evaluate the model with some metrics
  • How to train an KAN using the Pipeline
    • Import classes and define paths
    • Define scalers if needed
    • Create datasets
    • Model creation
    • Run the pipeline
    • Show plots
    • Evaluate the model with some metrics

previous

pyLOM.math

next

Example of parallel Proper Orthogonal Decomposition

Edit on GitHub

This Page

  • Show Source

© Copyright 2023-2025.

Created using Sphinx 7.4.7.

Built with the PyData Sphinx Theme 0.16.1.