improve intro

Author: casenave

docs/index.md

@@ -6,8 +6,30 @@
 
 ## Introduction
 
-PLAID (Physics-Learning AI Datamodel) is a flexible and extensible framework for representing and sharing datasets of physics simulations. PLAID defines a unified standard for describing simulation data and is accompanied by a library for creating, reading, and manipulating complex datasets across a wide range of physical use cases.
-The data model and library have initially been developped at SafranTech, the research center of [Safran group](https://www.safran-group.com/).
+### PLAID (Physics-Learning AI Datamodel): The Missing Layer for Scientific ML
+
+Keep your simulation data intact, query it intuitively, and transform it seamlessly for deep learning.
+
+PLAID is an open framework that makes it easy to represent and share datasets from complex physics simulations. It introduces a common standard for describing simulation data and comes with a library to create, explore, and manipulate complex datasets of physics similations. PLAID was first developed at SafranTech, the research and innovation center of [Safran Group](https://www.safran-group.com/).
+
+
+### Why Another Data Model?
+
+In machine learning, datasets are often treated as flat tables, sequences, or images. Standard frameworks — Hugging Face, PyTorch, TensorFlow — assume your data is already regular, homogeneous, and columnar. But in scientific and industrial applications, this assumption rarely holds:
+
+- Simulations produce hierarchical, multi-zone data.
+- Fields have heterogeneous shapes, types, and metadata.
+- Implicit conventions vary from one simulation to another.
+
+Traditional ML datasets are not designed to handle this complexity efficiently. Flattening, padding, or converting these structures into a standard tabular format can be error-prone, memory-intensive, and slow, and it often destroys critical information about the underlying physical structure.
+
+PLAID fills this gap by sitting upstream in the ML pipeline, bridging raw scientific data and ML-ready formats:
+
+1. Capture the full structure: PLAID preserves hierarchical, multi-field, multi-zone data, including metadata, defaults, and units.
+2. Simplify access: Intuitive APIs let you query fields, arrays, and derived quantities without flattening or rewriting your trees.
+3. Prepare for ML: When needed, PLAID can produce ML-ready datasets (PyTorch, PyG, or Hugging Face style), while keeping memory and computation efficient.
+
+In short: PLAID is not “just another dataset format.” It is a scientific data management layer, designed for the complex, heterogeneous, high-dimensional world of physics-based simulations, where preparing your data for ML is as important as the model itself.
 
 ## Open source