Examples

• How to train an MLP using the Pipeline
  • Import classes and define paths
  • Define sklearn scalers if needed
  • Create datasets
  • Evaluators and Plotters
  • Model creation
  • Run the pipeline
• How to optimize with optuna an MLP using the Pipeline
  • Import classes and define paths
  • Define sklearn scalers if needed
  • Create datasets
  • Evaluator
  • Optimization
  • Run the pipeline
• 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
• Train a PINN (Physics Informed Neural Network) Shrodinger equation
  • Define BC points and Collocation points
  • Define PDE loss function
  • Define Boundary conditions
  • Create Dataset
  • PINN and NN definiton
  • PINN training with Adam optimizer
  • PINN training with LBFGS optimizer
  • Save model