From f4ac8651d28c74be5e5ee83b2fc1df861ac62702 Mon Sep 17 00:00:00 2001
From: Varshini <119073242+VarshiniGunti@users.noreply.github.com>
Date: Tue, 3 Feb 2026 19:28:08 +0530
Subject: [PATCH] Add project structure section to main README

Added a "Project Structure" section to the main README to describe the repository layout and summarize each subproject.

This helps contributors understand:
- What each folder represents
- How subprojects relate to the GENIE initiative
- Where to find detailed documentation

This change improves repository navigation and onboarding clarity.
---
 README.md | 19 +++++++++++++++++++
 1 file changed, 19 insertions(+)

diff --git a/README.md b/README.md
index 3c9c0da..6eac929 100644
--- a/README.md
+++ b/README.md
@@ -1 +1,20 @@
 # GENIE
+
+## Project Structure
+
+This repository contains multiple experimental subprojects under the **GENIE** initiative within ML4Sci. Each subproject explores different approaches to physics-informed learning, graph neural networks, and diffusion-based modeling in high-energy physics.
+
+### Subprojects
+
+- **Graph_Representation_Learning_Rushil_Singha**  
+  Implements a JetNet Graph Diffusion Model using graph neural networks and latent diffusion for particle-level jet generation.  
+  See project README: `Graph_Representation_Learning_Rushil_Singha/README.md`
+
+- **Non_local_Jet_Classification_Tanmay_Bakshi**  
+  Explores graph neural network methods for jet classification tasks using non-local message passing architectures.
+
+- **Physics_Informed_Neural_Network_Diffusion (PINNDE)**  
+  A Physics-Informed Neural Network approach for solving reverse-time diffusion equations.  
+  Developed as part of a GSoC'25 project, PINNDE aims to build a fast and reliable sampler for complex probability distributions by combining diffusion models with physics-informed neural networks. The project demonstrates promising results in 1D, 2D, and 3D distributions and serves as a foundation for fast particle jet simulation methods.
+
+Each folder represents an independent research or experimental project. Contributors are encouraged to read the README within each subproject directory for setup instructions, usage details, and implementation information.