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Found 4 papers, 1 papers with code
Transformers as Neural Operators for Solutions of Differential Equations with Finite Regularity
Transformers have not been used in that capacity, and specifically, they have not been tested for solutions of PDEs with low regularity.
RiemannONets: Interpretable Neural Operators for Riemann Problems
Developing the proper representations for simulating high-speed flows with strong shock waves, rarefactions, and contact discontinuities has been a long-standing question in numerical analysis.
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.
Deep neural operators can serve as accurate surrogates for shape optimization: A case study for airfoils
Deep neural operators, such as DeepONets, have changed the paradigm in high-dimensional nonlinear regression from function regression to (differential) operator regression, paving the way for significant changes in computational engineering applications.
