Published December 18, 2024 | Version v1
Dataset Open

Multi-Frequency Far-Field Wave Scattering Data

Description

This is a dataset designed to train and evaluate deep learning methods for forward and inverse multi-frequency far-field wave scattering problems. In this dataset, we have pairs of 2D scattering potentials q and scattered wave field measurements d_k, measured at several incident wave frequencies k. We define a distribution of scattering potentials that have piecewise constant geometric shapes with an unknown low-frequency background. With this dataset, one can train machine learning models to solve the forward problem q -> d_k, or the inverse problem d_k-> q
 
Please see our article for a formal definition of the forward and inverse scattering problems: 
 
 
Melia, O., Tsang, O., Charisopoulos, V., Khoo, Y., Hoskins, J., Willett, R., 2025. Multi-frequency progressive refinement for learned inverse scattering. Journal of Computational Physics 527, 113809. https://doi.org/10.1016/j.jcp.2025.113809
 
Also see our code repository which was used to generate the data and train neural networks to solve the multi-frequency inverse problem: https://github.com/meliao/MFISNets
 
Once decompressed, our dataset has the following file structure.
data/
└── dataset/
    ├── train_measurements_nu_*/    # We have directories for nu={1,2,4,8,16}, equivalently k={2pi,4pi,8pi,16pi,32pi}
    │   └── measurements_*.h5       # each measurements_i.h5 has 500 scattering potentials.
    ├── val_measurements_nu_*/
    │   └── measurements_*.h5
    └── test_measurements_nu_*/
        └── measurements_*.h5

The measurement files are saved in hdf5 format, with the following fields:

  • q_cart: the scattering potentials sampled on a Cartesian grid.
  • q_polar: the scattering potentials sampled on a polar grid.
  • x_vals: 1d coordinates of the regular Cartesian grid for the scattering domain
  • rho_vals: radius values of the regular polar grid for the scattering domain
  • theta_vals: angular values of the regular polar grid for the scattering domain. Also used as the source/receiver directions when generating measurements.
  • seed: the RNG seed used when generating this file.
  • contrast: the maximum contrast setting.
  • background_max_freq: the maximum frequency parameter used when defining the random background part of the scattering potentials.
  • background_max_radius: the radius of the disk occupied by the background field.
  • num_shapes: how many piecewise-constant shapes were generated.
  • gaussian_lpf_param: parameter used to build Gaussian lowpass filter that slightly smooths the scattering potentials.
  • nu_sf: non-angular wavenumber (in space).
  • omega_sf: angular frequency (in time).
  • q_cart_lpf: scattering objects transformed by a Gaussian LPF, sampled on the Cartesian grid.
  • q_polar_lpf: scattering objects transformed by a Gaussian LPF, sampled on the polar grid.
  • d_rs: Measurements of the scattered wave field, in the original (receiver, source) coordinates.
  • d_mh: Measurements of the scattered wave field, in the (m, h) coordinates suggested by Fan and Ying, 2022.
  • m_vals: Coordinates of the (m, h) transformed data.
  • h_vals: Coordinates of the (m, h) transformed data.
  • sample_completion: array of booleans indicating whether individual samples were generated.
  • file_completion: single boolean set to True when the entire generation script is completed.

Files

Files (48.6 GB)

Name Size Download all
md5:0df25fd8a36cf49657302faf89140018
9.7 GB Download
md5:dd8c0cf1255ac6a2f1b0b2f92fbe4546
9.8 GB Download
md5:9dd7b47001976847a0bf777fefd8dfa3
9.7 GB Download
md5:91b4a07457a45641b5d1860f28d5f708
9.7 GB Download
md5:72eadd4197d1fad59fbeaaa07bc437d0
9.7 GB Download

Additional details

Related works

Is documented by
Publication: 10.1016/j.jcp.2025.113809 (DOI)

Funding

United States Air Force Office of Scientific Research
A9550-18-1-0166
U.S. National Science Foundation
DMS-2023109
United States Department of Energy
DE-SC0022232
U.S. National Science Foundation
DMS-2339439

Software

Repository URL
https://github.com/meliao/mfisnets
Programming language
Python