From 7b9c0262198765553972bbb21b68e6a9fafa0647 Mon Sep 17 00:00:00 2001
From: Ayush <ayushssp74@gmail.com>
Date: Wed, 10 Dec 2025 04:01:30 +0000
Subject: [PATCH] Docs: Added tutorial for PyTorch-SU2 coupling

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 create mode 100644 _tutorials/workflow_features/hybrid_ml_coupling.md

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+---
+layout: default
+title: Hybrid PyTorch-SU2 Coupling
+description: Examples of coupling SU2 with external ML libraries (PyTorch) via the Python Wrapper.
+group: "Workflow Features"
+---
+
+# Hybrid Physics-ML Coupling with PyTorch
+
+This tutorial demonstrates how to establish a bidirectional coupling between the SU2 solver and **PyTorch** using the standard Python Wrapper (`pysu2`).
+
+Unlike offline training (where data is saved to disk and trained later), this workflow allows for **online (in-situ) learning**, where a neural network is trained on flow physics as the simulation progresses.
+
+### Goals
+1.  Execute the SU2 solver step-by-step via Python.
+2.  Extract flow variables (e.g., `RMS_DENSITY`) from memory in real-time.
+3.  Train a PyTorch surrogate model inside the MPI loop.
+4.  Demonstrate the "Physics → ML" data bridge.
+
+### Resources
+The complete scripts and configuration files for this tutorial are available in the [SU2 Tutorials Repository](https://github.com/su2code/Tutorials/tree/master/PyWrapper/PyTorch_Coupling).
+
+### Prerequisites
+* **SU2** compiled with MPI and Python Wrapper support:
+    * `-Dwith-mpi=enabled`
+    * `-Denable-pywrapper=true`
+* **Python packages**: `mpi4py`, `torch`, `numpy`
+
+### The Coupling Loop
+The core of the workflow is the `CSinglezoneDriver`, which allows Python to control the C++ solver iteration-by-iteration.
+
+```python
+# Initialize Driver with MPI
+driver = pysu2.CSinglezoneDriver("inv_NACA0012.cfg", 1, MPI.COMM_WORLD)
+driver.Preprocess(0)
+
+# Training Loop
+for i in range(n_iterations):
+    # 1. Run Physics (SU2)
+    driver.Run()
+    
+    # 2. Extract Data (C++ -> Python)
+    physics_state = driver.GetOutputValue("RMS_DENSITY")
+    
+    # 3. Train Model (PyTorch)
+    # ... standard pytorch forward/backward pass ...
+    
+    # 4. (Optional) Feedback
+    # Update flow boundary conditions or deformations based on ML prediction.