Data for InverseBench (ICLR 2025 spotlight)  InverseBench: Benchmarking Plug-and-Play Diffusion Priors for Inverse Problems in Physical Sciences    Hongkai Zheng*, Wenda Chu*, Bingliang Zhang*, Zihui Wu*, Austin Wang, Berthy T. Feng, Caifeng Zou, Yu Sun, Nikola Kovachki, Zachary E. Ross, Katherine L. Bouman, Yisong Yue (*: Equal contribution)   ...

This repository contains the datasets corresponding to the three benchmark problems for the Fourier Neural Mappings scientific machine learning architectures. The first file is the data for the advection-diffusion problem, the second for the airfoil problem, and the third for the elliptic homogenization materials problem. The code associated wit...

This repository contains the datasets corresponding to the two benchmark problems appearing in the SIAM papers "The Random Feature Model for Input-Output Maps between Banach Spaces" [SIAM J. Sci. Comput., Vol. 43, No. 5 (2021), pp. A3212–A3243] and the paper "Operator learning using random features: a tool for scientific computing" [SIAM Review,...

DFT electron density computed on geometries of C7O2H10 isomers.   Data generated using the Gaussian Plane Waves DFT at the PBE/TZV2P/400Ry level by PySCF.   Each tar corresponds to one MD trajectory of an isomer, and geometries in each trajectory are numbered from 0 to 49. {0..49}_rho_22.npy: DFT density in a.u. {0..49}_grid_sizes_22.dat: shape ...

DFT electron density computed on geometries of QM9 molecules.   Data generated using the Gaussian Plane Waves DFT at the PBE/TZV2P/400Ry level by PySCF.   Each tar contains 1000 folders and each folder corresponds to one QM9 molecule. In each folder, rho_22.npy: DFT density in a.u. grid_sizes_22.dat: shape of the density energy_22.dat: DFT groun...