Search Results for author:
Found 5 papers, 0 papers with code
DiTTO: Diffusion-inspired Temporal Transformer Operator
We also present a method to improve the performance of DiTTO by using fast sampling concepts from diffusion models.
ViTO: Vision Transformer-Operator
We combine vision transformers with operator learning to solve diverse inverse problems described by partial differential equations (PDEs).
A Hybrid Iterative Numerical Transferable Solver (HINTS) for PDEs Based on Deep Operator Network and Relaxation Methods
Based on recent advances in scientific deep learning for operator regression, we propose HINTS, a hybrid, iterative, numerical, and transferable solver for differential equations.
A physically-informed Deep-Learning approach for locating sources in a waveguide
Inverse source problems are central to many applications in acoustics, geophysics, non-destructive testing, and more.
A Convolutional Dispersion Relation Preserving Scheme for the Acoustic Wave Equation
We propose an accurate numerical scheme for approximating the solution of the two dimensional acoustic wave problem.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
