HNO Helmholtz Scattering Dataset
收藏Zenodo2026-05-13 更新2026-05-26 收录
下载链接：
https://zenodo.org/doi/10.5281/zenodo.20151646
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资源简介：
Dataset associated with the manuscript "FFT-free Neural Operators for Helmholtz Scattering via Adaptive Basis Modulation" by Ju O Kim and Deokwoo Lee (Keimyung University), submitted to Applied Sciences (MDPI), Special Issue on "Physics-Informed Learning: Applications in Physics-Informed Neural Networks and Machine Learning".
The dataset contains 2D Helmholtz scattering solutions on a 128x128 grid, generated by a 5-point finite-difference solver with Perfectly Matched Layer (PML) boundary conditions, organized into 4 complexity levels (single scatterer to high-contrast OOD scenarios). Total: ~497 MB across 4 HDF5 files.
Each HDF5 file contains the refractive-index field n^2(x) and the complex wave field (real and imaginary parts) for multiple samples at k0 = 20. See README.md for the full schema, Python usage example, and train/test split convention used in the paper.
提供机构：
Zenodo
创建时间：
2026-05-13
