Update README.md

Author: Paul-Saves

README.md

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-# SMT Design Space Extension
 
-This module allows to use advanced design space implementation for hierachical variables handling.
+# SMT Design Space Extension 🌳
+
+**Enhancing SMT with Hierarchical & Conditional Design Spaces**
+
+This package is an extension to the SMT (Surrogate Modeling Toolbox), offering a powerful framework to define and manage **hierarchical**, **mixed-type**, and **conditionally active** variables in design spaces.
+
+---
+
+## 🔍 What It Does
+
+- **Hierarchical variables**: Support for nested conditional variables (e.g., a rotor configuration branch that only activates when `use_rotor = yes`).
+- **Mixed types**: Handles continuous, integer, categorical, meta and indicator variables uniformly.
+- **Conditional activation**: Meta-variables powerfully control lower-level variable activation based on context.
+- **Graph-based design space representation**: Clean and intuitive implementation of complex, branching designs.
+- **Extensible subtress**: Easily add new types or layers of conditional logic.
+
+---
+
+## 📖 Why It Matters
+
+In many engineering and simulation contexts, the design parameters form a structured, dependent hierarchy—optimizing them without a formal framework can lead to errors or inefficient models. This extension provides:
+
+- A **formal representation** of hierarchical dependencies.
+- Seamless integration with surrogate modeling tools and optimization routines.
+- A foundation for improved experimental design and architectural exploration.
+
+---
+
+## 🔗 Reference
+
+This implementation is based on:
+
+> **Hierarchical Modeling and Architecture Optimization: Review and Unified Framework**  
+> P. Saves, E. Hallé‑Hannan, J. Bussemaker, Y. Diouane, N. Bartoli (June 2025). arXiv:2506.22621 ([arXiv](https://arxiv.org/abs/2506.22621))
+
+The paper provides a comprehensive survey and introduces a unified graph-based model that serves as the theoretical foundation for this extension.
+
+---
+
+## 📦 Installation
+
+```bash
+pip install smt-design-space-ext
+```
+
+---
+
+## 🚀 Quick Start Example
+
+```python
+from smt_design_space import DesignSpace
+
+ds = DesignSpace()
+# Meta-variable to enable feature branching
+ds.add_categorical("enable_feature", ["yes", "no"])
+# Feature type only active if enabled
+ds.add_meta("feature_type",
+            parent="enable_feature",
+            active_if="yes",
+            values=["typeA", "typeB"])
+# Parameter only active in typeA branch
+ds.add_numeric("featureA_param",
+               parent="feature_type",
+               active_if="typeA",
+               bounds=(0.0, 1.0))
+```
+
+This dynamically builds a tree-like structure of variables, enabling clear and constrained space exploration.
+
+---
+
+## 🧩 Integration
+
+- Integrates readily with SMT’s Kriging and PyKriging modules.
+- Compatible with Bayesian, evolutionary, or gradient‑based optimizers.
+- Prepare space definitions for use with graph neural networks or surrogate modeling pipelines.
+
+---
+
+## 📚 Additional Resources
+
+- The **SMT repository** contains tutorials and example notebooks to demonstrate usage.
+- Issue tracker and usage discussions can be found under `SMTorg/smt-design-space-ext`.
+- Author metadata and license summary confirm this is BSD‑licensed.
+
+---
+
+## ✅ Citation
+
+If you use this package in research, please cite:
+
+```
+P. Saves, E. Hallé‑Hannan, J. Bussemaker, Y. Diouane, N. Bartoli,
+“Hierarchical Modeling and Architecture Optimization: Review and Unified Framework,”
+arXiv:2506.22621, June 2025.
+```
+
+---
+
+## 📜 License
+
+Distributed under the **BSD-3-Clause** license. See [LICENSE](LICENSE) for details.