The repository contains codes for Universal Functional Regression with Neural Operator Flows
PyTorch 1.12.1
scikit-learn 1.2.2
| Files | Descriptions |
|---|---|
| Generation tasks | |
| 1D_domain_decomposed_GP.ipynb | resolution=256, generation task for 1D GP data |
| 1D_domain_decomposed_TGP.ipynb | resolution=256, generation task for 1D Truncated GP data |
| 2D_domain_decomposed_GRF.ipynb | resolution=64x64, generation task for 2D GRF data |
| 2D_domain_decomposed_TGRF.ipynb | resolution=64x64, generation task for 2D Truncated GRF data |
| 1D_codomain_GP.ipynb | resolution=256, sliding regularization used, generation task for 1D GP data, codomain OpFlow |
| 2D_codomain_TGRF.ipynb | resolution=64x64, generatin tasks for 2D Truncated GRF data, codomain OpFlow |
| Regression tasks | |
| 1D_domain_decomposed_GP_prior.ipynb | resolution=128 |
| 1D_domain_decomposed_GP_regression.ipynb | duplicate the results of classical GPR |
| 1D_domain_decomposed_TGP_prior.ipynb | resolution=128 |
| 1D_domain_decomposed_TGP_regression.ipynb | Non-Gaussian process regression |
| 2D_domain_decomposed_GRF_prior.ipynb | resolution=32x32 |
| 2D_domain_decomposed_GRF_regression_case1.ipynb | regression with scatter observations |
| 2D_domain_decomposed_GRF_regression_case2.ipynb | regression with strip observations |
| 1D_codomain_GP_prior.ipynb | resolution=128 |
| 1D_codomain_GP_regression.ipynb | codomain GP Regression |
| SGLD sampling | |
| samplers.py | |
| SGLD.py | |
| Comments | sliding regularization trick used in some files can be useful for others challenging tasks, feel free to add that on for all tasks |
Synthetic dataset can be directly generated in the training files, earthquake datasets used in the paper can be downloaded from kik-net website kik-net
@article{shi2024universal, title={Universal Functional Regression with Neural Operator Flows}, author={Shi, Yaozhong and Gao, Angela F and Ross, Zachary E and Azizzadenesheli, Kamyar}, journal={arXiv preprint arXiv:2404.02986}, year={2024} }