Transformers as neural operators for solutions of differential equations with finite regularity
Neural operator learning models have emerged as very effective surrogates in data-driven
methods for partial differential equations (PDEs) across different applications from …
methods for partial differential equations (PDEs) across different applications from …
Newton Informed Neural Operator for Solving Nonlinear Partial Differential Equations
Solving nonlinear partial differential equations (PDEs) with multiple solutions is essential in
various fields, including physics, biology, and engineering. However, traditional numerical …
various fields, including physics, biology, and engineering. However, traditional numerical …
Dual-branch neural operator for enhanced out-of-distribution generalization
J Li, M Yang - Engineering Analysis with Boundary Elements, 2025 - Elsevier
Neural operators, which learn map**s between function spaces, offer an efficient
alternative for solving partial differential equations. However, their generalization to out-of …
alternative for solving partial differential equations. However, their generalization to out-of …
CaFA: Global Weather Forecasting with Factorized Attention on Sphere
Accurate weather forecasting is crucial in various sectors, impacting decision-making
processes and societal events. Data-driven approaches based on machine learning models …
processes and societal events. Data-driven approaches based on machine learning models …
Modeling Multivariable High-resolution 3D Urban Microclimate Using Localized Fourier Neural Operator
Accurate urban microclimate analysis with wind velocity and temperature is vital for energy-
efficient urban planning, supporting carbon reduction, enhancing public health and comfort …
efficient urban planning, supporting carbon reduction, enhancing public health and comfort …