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OneScience-Group/beno

BENO Dataset Description The BENO dataset originates from the ICLR 2024 paper BENO: Boundary-Embedded Neural Operators for Elliptic PDEs and is designed for solving elliptic partial differential equations under complex boundary conditions. The data contains random boundary geometries with four, three, two, one, or no corners, all standardized to a 32 x 32 grid resolution. Paper: BENO: Boundary-Embedded Neural Operators for Elliptic PDEs Supported Tasks… See the full description on the dataset page: https://huggingface.co/datasets/OneScience-Group/beno.

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Dataset Card

BENO

Dataset Description

The BENO dataset originates from the ICLR 2024 paper BENO: Boundary-Embedded Neural Operators for Elliptic PDEs and is designed for solving elliptic partial differential equations under complex boundary conditions. The data contains random boundary geometries with four, three, two, one, or no corners, all standardized to a 32 x 32 grid resolution.

Paper: BENO: Boundary-Embedded Neural Operators for Elliptic PDEs

Supported Tasks

Scenario	Description
Elliptic PDE solving	Predict the solution field from the boundary conditions and the right-hand side of the equation.
Boundary-condition research	Compare solution performance under Dirichlet and Neumann boundary conditions.
Geometry generalization evaluation	Evaluate model generalization across different random boundary shapes.
Neural operator research	Provide standardized training and evaluation data for operator models such as BENO.

Dataset Format and Structure

The data is organized by boundary condition:

text

data/
  Dirichlet/
  Neumann/

Each boundary-condition category contains the following six configurations: N32_0c, N32_1c, N32_2c, N32_3c, N32_4c, and N32_mix. Each configuration contains 1,000 float64 samples:

File	shape	Description
`BC_<prefix>_all.npy`	`[1000, 128, 4]`	Boundary coordinates, boundary values, and boundary features.
`RHS_<prefix>_all.npy`	`[1000, 1024, 4]`	Coordinates, source terms, and cell states on a 32×32 grid.
`SOL_<prefix>_all.npy`	`[1000, 1024, 1]`	Elliptic PDE solution fields.

How to Use the Dataset

This dataset has been adapted for the OneScience-Group/BENO model. Download the dataset and model:

bash

hf download --dataset OneScience-Group/beno --local-dir ./data

Official OneScience Information

Platform	OneScience Main Repository	Skills Repository
Gitee	https://gitee.com/onescience-ai/onescience	https://gitee.com/onescience-ai/oneskills
GitHub	https://github.com/onescience-ai/OneScience	https://github.com/onescience-ai/oneskills

Citation and License

—Original BENO paper: BENO: Boundary-embedded Neural Operators for Elliptic PDEs
—This dataset has been organized and converted from the original BENO dataset. Its use must comply with the licensing requirements published by the original project.