[Resource] Hybrid PyTorch-SU2 Coupling with Python Wrapper (Example & Docs)

[ayush4874]

Hi everyone,

I've been working on setting up an online (in-situ) coupling between SU2 and PyTorch using the Python Wrapper, and I noticed there wasn't a standalone example for this workflow in the tutorials yet.

I've successfully prototyped a bidirectional loop where SU2 runs within an MPI process, and a PyTorch model extracts flow physics (like RMS_DENSITY) in real-time.

To help others getting started with Physics-ML coupling, I've submitted the scripts and documentation to the official repositories:

• Tutorial Code: su2code/Tutorials#71 (Adds train_online.py and configs)
• Documentation: su2code/su2code.github.io#189 (Explains the CSinglezoneDriver loop)

Current Limitation & Next Steps:
Currently, this workflow is excellent for extraction (Physics -> ML). However, I've noticed that injecting updated weights back into the C++ MLPCpp module during runtime is tricky due to encapsulation.

I am currently prototyping a C++ patch to allow dynamic weight injection (Online Learning), which I hope to propose for GSoC 2026.

I'd love any feedback from those working on data-driven surrogates!

Best,
Ayush