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README.md

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 ---
-license: mit
+license: apache-2.0
+task_categories:
+- time-series-forecasting
+- other
+tags:
+- physics
+- pde
+- ode
+- simulation
+- world-models
+- neural-surrogate
+- trust-signal
+- reaction-diffusion
+- euler-equations
+- rigid-body
+pretty_name: Hybrid Neural World Models
+size_categories:
+- 10K<n<100K
 ---
+
+# Hybrid Neural World Models — datasets
+
+Training, validation, test, and out-of-distribution (OOD) trajectories for the
+three physical systems used in **Hybrid Neural World Models** (Pranav Lakshmanan,
+Paras Chopra). The accompanying code, checkpoints, and paper define a single
+neural surrogate that predicts states at any horizon plus a *step-doubling*
+trust signal that flags when its forecasts can be trusted.
+
+This repo contains the raw trajectory data only. Models / training code live
+separately.
+
+## What's inside
+
+```
+.
+├── oregonator/                          reaction-diffusion PDE (Belousov-Zhabotinsky)
+│   ├── oregonator_train.h5              1200 traj × 201 steps × 2 ch × 256 × 256
+│   ├── oregonator_val.h5                 150 traj
+│   ├── oregonator_test.h5                150 traj
+│   ├── oregonator_ood_near.h5            250 traj   (mild parameter shift)
+│   ├── oregonator_ood_far.h5             250 traj   (stronger parameter shift)
+│   └── dataset_config.json
+├── euler2d/                             compressible flow PDE
+│   ├── euler2d_v2_train.h5
+│   ├── euler2d_v2_val.h5
+│   ├── euler2d_v2_test.h5
+│   ├── euler2d_v2_ood_near.h5
+│   └── euler2d_v2_ood_far.h5
+└── ball3d/                              rigid-body bouncing ODE (MuJoCo)
+    ├── ball3d_train.h5                  1000 traj × 101 steps × 9 dims
+    ├── ball3d_val.h5
+    ├── ball3d_test.h5
+    ├── ball3d_ood_near.h5
+    └── ball3d_ood_far.h5
+```
+
+## Environments
+
+### 1. Reaction-diffusion fronts (Oregonator / BZ)
+
+Two-variable Tyson Oregonator in non-dimensional units (D = 1, ε = 0.05,
+q = 0.002, f = 2.0) integrated on a 256 × 256 periodic grid with explicit-FV
+spatial stencils and Strang splitting. Each trajectory captures 201 saved
+frames (Δt_save = 0.05, total time = 10.0) of the two chemical concentrations
+(`u`, `v`). Initial conditions are a mix of spiral, target-pattern, and
+random superpositions — see `oregonator/dataset_config.json` for ratios and
+seed offsets.
+
+State shape per saved frame: `(2, 256, 256)` float32.
+
+### 2. Compressible gas flow (Euler 2D)
+
+2D compressible Euler equations solved with an MUSCL-Hancock + HLLC scheme on
+a 128 × 128 grid. Initial conditions cover Schulz-Rinne Riemann
+configurations and shock-interaction setups. Each trajectory: 100 saved frames
+of `(ρ, ρvx, ρvy, E)` — i.e. four conservative fields stored flattened to
+`16 384` per timestep (reshape to `(128, 128, 4)` if you want per-channel
+imagery).
+
+### 3. Rigid-body bouncing (Ball 3D)
+
+MuJoCo simulation of a single ball bouncing inside a 1 × 1 × 0.6 m box with
+elastic-ish contact. State is a 9-vector per frame:
+
+| dim   | quantity              |
+|-------|------------------------|
+| 0–2   | position `(x, y, z)`   |
+| 3–5   | linear velocity        |
+| 6–8   | angular velocity       |
+
+Trajectories: 101 frames at fixed Δt.
+
+## OOD splits
+
+- `*_ood_near.h5` — parameters drawn from a moderately shifted distribution
+  relative to `train`. Used to test robustness in the paper's "OOD-near" cells.
+- `*_ood_far.h5` — stronger shift. Used in "OOD-far" cells. Errors are larger;
+  the trust signal stays informative (AUROC ≥ 0.65) and is the main argument
+  for the trust-gated fallback (Mode 2).
+
+## Quick start
+
+```python
+import h5py
+
+with h5py.File("oregonator/oregonator_test.h5", "r") as f:
+    states  = f["states"]       # (N, T, 2, 256, 256), float32
+    params  = f["params"]       # (N, 4)              IC + physics params
+    seeds   = f["seeds"]        # (N,)
+    ic_types= f["ic_types"]     # (N,)                spiral / target / random
+    print(states.shape, states.dtype)
+    snapshot_u = states[0, 40, 0]  # u-concentration of traj 0 at t=40
+
+with h5py.File("ball3d/ball3d_test.h5", "r") as f:
+    states = f["states"][:]     # (200, 101, 9)
+```
+
+Euler trajectories live under per-trajectory groups inside the file; iterate
+keys to enumerate.
+
+## Reproducing splits
+
+Each split is defined by a base seed offset (see `dataset_config.json`) so
+splits don't share initial conditions and can be regenerated bit-for-bit:
+
+| split    | seed offset |
+|----------|------------:|
+| train    | 0           |
+| val      | 100 000     |
+| test     | 200 000     |
+| ood_near | 300 000     |
+| ood_far  | 400 000     |
+
+## Intended use
+
+- Benchmarking neural surrogates that predict any horizon in a single forward
+  pass.
+- Studying label-free uncertainty / trust-signal methods for physics models.
+- Cross-system generalization studies (one set of trajectories per env, same
+  splits across all three).
+
+## Citation
+
+If you use these datasets please cite:
+
+```
+@misc{hybrid_neural_world_models_2026,
+  title   = {Hybrid Neural World Models for Physical Dynamics},
+  author  = {Lakshmanan, Pranav and Chopra, Paras},
+  year    = {2026},
+}
+```
+
+## Contact
+
+[lossfunk](https://x.com/lossfunk) — open an issue on the repo or DM on X.