Inverse PINN to takle this problem
Hi everyone, I hope you are doing well.
I am exploring the idea of using a Physics-Informed Neural Network (PINN) on this dataset to perform inverse design for optimal stellarator geometries, and I'm curious if anyone here has tried a similar approach.
My proposed methodology is a two-step pipeline:
Feature Selection & Correlation: Because the parameter space of the boundary Fourier coefficients (r_cos, z_sin) is so massive, my first step is to run an exploratory analysis. Since plasma physics is highly non-linear, I plan to use tree-based feature importance (like SHAP values) alongside standard correlation matrices. The goal is to isolate exactly which geometric inputs act as the primary drivers for the main physics objectives (Quasisymmetry error, Rotational Transform, Aspect Ratio, etc.).
PINN Inverse Design: Once I have isolated the most influential parameters, I plan to encode those relationships directly into the loss function of a PINN as physical constraints, allowing the network to output an optimized geometry.
If anyone has tackled a similar pipeline, I would love to discuss with them.