Update index.md

Author: casenave

docs/index.md

@@ -1,44 +1,22 @@
-# Physics-Learning AI Datamodel
+# PLAID — Physics Learning AI Data Model
+**The missing data layer between physics simulations and scientific ML.**
 
-<!-- <p style="text-align:center;">
-  <img src="../assets/images/plaid_logo2.png" alt="logo" title="Logo" style="width:40%;"/>
-</p> -->
+PLAID is an open framework for representing, sharing, and learning from datasets of complex physics simulations. It defines a common standard for simulation data and ships a Python library to create, explore, store, and stream them.
 
-## Introduction
+## Why another data model?
 
-### PLAID (Physics-Learning AI Datamodel): the missing layer for Scientific ML
+Mainstream ML stacks (Hugging Face, PyTorch, TensorFlow) assume data is regular, homogeneous, and columnar. Real simulation data is not: it is **hierarchical and multi-zone**, with **heterogeneous fields, shapes, and metadata**, often governed by **implicit, solver-specific conventions**. Flattening or padding it into tabular form is error-prone, memory-hungry, and erases the physical structure the model should learn from.
 
-Keep your simulation data intact, query it intuitively, and transform it seamlessly for deep learning.
+## What PLAID provides
 
-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 may 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, including graph-based ones like PyTorch Geometric (PyG):
-
-1. Capture the full structure: PLAID preserves hierarchical, multi-field, multi-zone data, including metadata.
-2. Simplify access: intuitive APIs let you query fields, arrays, and derived quantities without flattening or rewriting your trees.
-3. Prepare for ML: PLAID can generate PyTorch datasets, Hugging Face datasets, or PyG graph objects, so batching and training pipelines work seamlessly, 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 (whether columnar or graph-structured) is as important as the model itself.
-
-## Open source
-
-The open source project is hosted on GitHub under the [PLAID-lib](https://github.com/PLAID-lib) organization. The project currently contains three librairies:
-
-- [PLAID](https://github.com/PLAID-lib/plaid) is the core library of the PLAID project.
-- [PLAID-bridges](https://github.com/PLAID-lib/plaid-bridges): provides integrations into machine learning frameworks such as PyTorch Geometric.
-- [PLAID-ops](https://github.com/PLAID-lib/plaid-ops): a collection of standardized operations on PLAID samples and datasets, including advanced treatments on meshes (some requiring a finite-element engine) powered by [muscat](https://gitlab.com/drti/muscat).
-
-## Paper
-
-Check out the [PLAID preprint](https://arxiv.org/abs/2505.02974) recently submitted to arXiv!
+- **Fidelity** — Keep all the complexity of your simulation data — meshes, fields, tags, time, and
+    multiphysics structure — and exploit it directly in ML pipelines.
+- **Out-of-core datasets** — Datasets are accessed sample by sample, so full datasets do not need to be loaded
+    into memory.
+- **Parallel I/O** — `save_to_disk` can shard sample IDs across multiple processes for fast dataset
+    generation and writing.
+- **Multiple storage backends** — Use **CGNS**, **Hugging Face Datasets**, or **Zarr** through a unified API for
+    local disk, Hub download, and streaming workflows.
+- **Selective reading** — Request only the features you need and, when necessary, only selected indices within large variable arrays.
+- **Interactive viewer** — Launch `plaid-viewer` to browse local or streamed datasets, inspect samples in 3D,
+    select features, and visualize fields.