Rename 'Training' to 'Model Training' and expand details

Updated section title and added details about model training process.

Author: sachdevakunal

_projects/ms_thesis.md

@@ -64,11 +64,15 @@ link: /projects/ms-thesis/
   <img src="https://sachdevakunal.github.io/images/ms_thesis/Operator_splitting_schematic.png" width="75%">
 </p> 
 
-## Training
+## Model Training
 
 - The training data was generated using a 2D backward-facing step methane-air flame simulated with the DRM19 mechanism, providing diverse reacting-flow conditions.
 - A coarse-grid simulation was used to collect temporal snapshots of temperature and species fields, from which representative species were selected based on a cutoff mass-fraction threshold.
 - PCA was applied to the sampled snapshots to select diverse thermochemical states for training while reducing redundancy in the dataset.  
 - Each sampled state was evolved through 0D constant-pressure reactors in Cantera to obtain time-series data for temperature and species evolution.  
 - The DeepONet and RecNet models were built in JAX and trained on an NVIDIA RTX A6000 GPU using a two-stage process: pre-training followed by refined auto-regressive training.  
 - Pre-training minimized short-step prediction error for physical consistency, while refined training increased rollout length by feeding the model’s own predictions back as inputs.
+
+- <p align="center">
+  <img src="https://sachdevakunal.github.io/images/ms_thesis/training_workflow.png" width="75%">
+</p>