nvidia/Linear-Radiation-Transport

Dataset Description:

A point-cloud surrogate-modeling dataset for the final-time 2-D linear Radiation Transport Equation (RTE), covering two canonical benchmarks that vary along complementary axes:

• Lattice (707 samples, 494 train / 106 val / 107 test) — fixed 7 × 7 block geometry; per-sample variation in the white-background scattering coefficient (σ_s ∈ [0.1, 10.1]) and the blue-absorber cross-section (σ_a ∈ [5, 105]). QoI: final-time absorption integral ${\int }_B{\sigma }_a\varphi dx\backslash int\_B\; \backslash sigma\_a\backslash ,\; \backslash phi\backslash ,\; dx$ over the absorbing blocks.

  

• Hohlraum (846 samples, 592 train / 126 val / 128 test) — fixed per-region cross-sections; per-sample variation in 8 geometry parameters (ulr, llr, urr, lrr, hlr, hrr, cx, cy) controlling the inner edges and y-extents of two wall-anchored red strips and the (x, y) offset of a center insert. QoI: final-time absorption integral ${\int }_S{\sigma }_a\varphi dx\backslash int\_S\; \backslash sigma\_a\backslash ,\; \backslash phi\backslash ,\; dx$ evaluated over three material regions $S\in \{\text{center insert},\text{ vertical strip},\text{ horizontal strip}\}S\; \backslash in\; \backslash \{\backslash text\{center\; insert\},\backslash \; \backslash text\{vertical\; strip\},\backslash \; \backslash text\{horizontal\; strip\}\backslash \}$.

  

Simulations were produced with KiT-RT using a discrete-ordinate (S_N) angular discretization, a finite-volume scheme on an unstructured mesh, and an explicit SSP Runge-Kutta time integrator, then curated into the PhysicsNeMo Mesh memmap format.

How to download

The dataset is not a datasets-loadable Parquet dataset; it ships PhysicsNeMo tensordict memmap stores packed as per-sample .pmsh.tar.gz archives. Download the full repo and extract the archives in place:

import tarfile
from pathlib import Path
from huggingface_hub import snapshot_download

local_dir = Path(snapshot_download(
    repo_id="nvidia/Linear-Radiation-Transport",
    repo_type="dataset",
))

for arc in (local_dir / "mesh").rglob("*.pmsh.tar.gz"):
    with tarfile.open(arc) as tf:
        tf.extractall(arc.parent)

After extraction each <name>.pmsh/ directory is loadable with PhysicsNeMo's Mesh API.

Dataset Owner(s):

NVIDIA Corporation

Dataset Creation Date:

May 2026

License/Terms of Use:

CC BY 4.0

Intended Usage:

Training, evaluation, and benchmarking of point-cloud / mesh-based neural surrogates for final-time linear radiation transport. The two benchmarks are complementary stress tests: Lattice probes the surrogate's ability to generalise across material parameters at fixed geometry, while Hohlraum probes generalisation across geometry at fixed material parameters. Suitable for graph neural networks, neural operators, point-cloud regressors, and mixed-fidelity / uncertainty-quantification studies that build on KiT-RT reference solutions.

Dataset Characterization

** Data Collection Method

• [Synthetic] - High-resolution KiT-RT (S_N + finite-volume) simulations on unstructured triangular meshes, post-processed into PhysicsNeMo Mesh memmap stores.
  

** Labeling Method

• [Synthetic] - Per-cell scalar flux and derived per-region absorption QoIs are computed directly by the numerical solver; no human labeling is involved.
  

Dataset Format

• Modality: 2-D point cloud / unstructured-mesh, per-cell tensors plus per-simulation scalar metadata.
• Per-sample container: PhysicsNeMo Mesh (a tensordict memmap store) shipped on disk as a <name>.pmsh/ directory plus a <name>.attrs.json sidecar; on the Hub each simulation is bundled as a single <name>.pmsh.tar.gz archive for transport.
• Per-cell fields: cell_areas (float32), sigma_a, sigma_s, sigma_t (float32), Q (float32), material_properties (int64), scalar_flux (float32, shape (N, 2) for initial + final snapshots).
• Cell-center coordinates: Mesh.points (float32, (N, 2) — the simulations are 2-D so points are stored without a z column).
• Per-simulation fields (Mesh.global_data): sim_times / timesteps / wall_times, flux_statistics, global_metrics, plus flattened attr__* parameter draws.
• Splits: JSON files at splits/{lattice,hohlraum}_splits.json storing per-split lists of sample basenames.
• Auxiliary: PNG schematics under docs/images/, conversion manifests at mesh/{lattice,hohlraum}/conversion_manifest.json.

Dataset Quantification

• Record count: 1,553 simulations covered by the train/val/test splits (707 Lattice + 846 Hohlraum).
• Cells per sample: lattice ≈79.9k (constant); hohlraum ≈70k–81k.
• Per-cell features per sample: 7 fields (cell_areas, sigma_a, sigma_s, sigma_t, Q, material_properties, scalar_flux) plus 2-D cell-center coordinates and per-simulation metadata.
• Total storage: ~6.0 GB for the extracted .pmsh/ directories; ~2.4 GB as the per-sample .pmsh.tar.gz archives shipped to the Hugging Face Hub (gzip-compressed).

Reference(s):

• Schotthöfer, S., & Hauck, C. (2025). "Reference solutions for linear radiation transport: the Hohlraum and Lattice benchmarks." arXiv preprint arXiv:2505.17284.
• Kusch, J., Schotthöfer, S., Stammer, P., Wolters, J., & Xiao, T. (2023). "KiT-RT: An extendable framework for radiative transfer and therapy." ACM Transactions on Mathematical Software, 49(4), 1–24.
• KiT-RT solver: https://github.com/KiT-RT.

Ethical Considerations:

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal developer teams to ensure this dataset meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
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