Sample Dataset and Trained Model Parameters for Back-Projection Diffusion

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}}我们已上传用于训练和测试反投影扩散（Back-Projection Diffusion）的示例数据集，该数据集对应的已训练模型参数亦一并收录于tmp.zip压缩包中。 若需获取该数据集的正式描述，请参阅我们的论文： 张博荣（Borong Zhang）、马丁·格拉（Martin Guerra）、李琴（Qin Li）、莱昂纳多·泽佩达-努涅斯（Leonardo Zepeda-Núñez）. 反投影扩散：利用扩散模型（diffusion models）求解宽带逆散射问题（wideband inverse scattering problem）[J]. 应用力学与工程计算方法, 2025, 443: 118036. https://doi.org/10.1016/j.cma.2025.118036 在10hsquares_trainingdata与10hsquares_testdata目录下，扰动数据以eta.h5格式存储，其内部结构为： eta.h5/ ├── /eta 散射数据可存储为scatter.h5，或以scatter_order_n.h5格式存储（其中n代表数据生成时所用的模板（stencil）阶数），其内部结构为： 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 tmp文件夹中包含该模型的已训练参数。 如需查看使用说明，请访问我们的GitHub仓库：https://github.com/borongzhang/back_projection_diffusion 若本数据集对你的研究有所助益，请引用如下论文： @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}}