We also present a method to improve the performance of DiTTO by using fast sampling concepts from diffusion models.
We combine vision transformers with operator learning to solve diverse inverse problems described by partial differential equations (PDEs).
Based on recent advances in scientific deep learning for operator regression, we propose HINTS, a hybrid, iterative, numerical, and transferable solver for differential equations.
Inverse source problems are central to many applications in acoustics, geophysics, non-destructive testing, and more.
We propose an accurate numerical scheme for approximating the solution of the two dimensional acoustic wave problem.