Transformers as neural operators for solutions of differential equations with finite regularity
From MaRDI portal
Publication:6669055
DOI10.1016/j.cma.2024.117560MaRDI QIDQ6669055
Ahmad Peyvan, Zhongqiang Zhang, George Em. Karniadakis, Benjamin Shih
Publication date: 22 January 2025
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
Cites Work
- Unnamed Item
- On numerical approximations of fractional-order spiking neuron models
- A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data
- SVD perspectives for augmenting DeepONet flexibility and interpretability
- Local approximation of operators
- Front tracking for hyperbolic conservation laws
- Transformers for modeling physical systems
- Mesh-informed neural networks for operator learning in finite element spaces
- A corrected \(\mathrm{L}1\) method for a time-fractional subdiffusion equation
- Convergence rate of DeepONets for learning operators arising from advection-diffusion equations
- Mitigating spectral bias for the multiscale operator learning with hierarchical attention
- RiemannONets: interpretable neural operators for Riemann problems
This page was built for publication: Transformers as neural operators for solutions of differential equations with finite regularity