MagPI was developed in the course of my PhD project at WPI and the Research Platform MMM at University of Vienna within the research group of Dr. Lukas Exl. It is a collection of useful algorithms and methods for the training of Physics Informed Neural Networks (PINNs). This is considered to be a starting point to a larger modular PINN framework for large scale full 3d micromagnetic simulations.
Here is a short summary of the features which are offered by MagPI at this point:
- Preliminary implementation of a CSG modelling framework using R-Functions
- A Trust Region optimizer with CGSteihaug solver
- Differential operators using forward mode AD
- A basic differentiable integration module
- Efficient Hessian vector products
- An differentiable ode solver
- Implementation of Quaternion rotation which can be used for CSG
- Some simple domains and useful transformations
- Simple importance sampling algorithm
Financial support by the Austrian Science Fund (FWF) via project P-31140 ”Reduced Order Approaches for Micromagnetics (ROAM)” and project P-35413 ”Design of Nanocomposite Magnets by Machine Learning (DeNaMML)” is gratefully acknowledged.