PLAID — Physics Learning AI Data Model¶
The missing data layer between physics simulations and scientific ML.
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.
Why another data model?¶
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.
What PLAID provides¶
- 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_diskcan 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-viewerto browse local or streamed datasets, inspect samples in 3D, select features, and visualize fields.