We have uploaded a sample dataset for training and testing Back-Projection Diffusion. Trained model parameters for the dataset are also provided in tmp.zip. For a formal description of the dataset, please refer to our paper: Zhang, B., Guerra, M., Li, Q., & Zepeda-Núñez, L. (2025). Back-Projection Diffusion: Solving the wideband inverse scattering problem with diffusion models. Computer Methods in Applied Mechanics and Engineering, 443, 118036. https://doi.org/10.1016/j.cma.2025.118036 In 10hsquares_trainingdata and 10hsquares_testdata, perturbations are stored as eta.h5 with the following structure: eta.h5/ ├── /eta The scattering data are stored as scatter.h5, or as scatter_order_n.h5 (n indicates the order of the stencil used for data generation) with the following structure: scatter.h5/ ├── /scatter_imag_freq_1 ├── /scatter_real_freq_1 ├── /scatter_imag_freq_2 ├── /scatter_real_freq_2 ├── /scatter_imag_freq_3 ├── /scatter_real_freq_3 The tmp folder contains the trained model parameters. For usage instructions, please refer to our GitHub repository: https://github.com/borongzhang/back_projection_diffusion If this dataset is useful to your research, please cite our paper:@article{ZHANG2025118036,title = {Back-Projection Diffusion: Solving the wideband inverse scattering problem with diffusion models},journal = {Computer Methods in Applied Mechanics and Engineering},volume = {443},pages = {118036},year = {2025},issn = {0045-7825},doi = {https://doi.org/10.1016/j.cma.2025.118036},url = {https://www.sciencedirect.com/science/article/pii/S0045782525003081},author = {Borong Zhang and Martin Guerra and Qin Li and Leonardo Zepeda-Núñez},keywords = {Machine learning, Inverse scattering, Generative modeling, Wave propagation, Diffusion models}}