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Found 5 papers, 0 papers with code
Real-time Inference and Extrapolation via a Diffusion-inspired Temporal Transformer Operator (DiTTO)
The proposed method, named Diffusion-inspired Temporal Transformer Operator (DiTTO), is inspired by latent diffusion models and their conditioning mechanism, which we use to incorporate the temporal evolution of the PDE, in combination with elements from the transformer architecture to improve its capabilities.
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.
