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.
Good idea
would like to work on that together?
Yes, I’d be interested in discussing this together.
I have actually tried a simple neural-network surrogate on a cleaned and simplified version of the dataset here: https://huggingface.co/datasets/SII-AI4Fusion/SII-Stellarator-Configuration-Dataset
It achieved fairly good accuracy on the validation/test split, but the main issue I observed is poor generalization in unfamiliar or out-of-distribution parameter regions. So I’m also thinking about whether introducing physics-informed constraints, or a PINN-like loss, could help improve the model’s robustness for inverse design.
Maybe we can compare notes on this. I think the key question is how to define the “physics-informed” part properly for this dataset, since the available data are mostly geometry-to-metric mappings rather than direct PDE residuals.