Transformer has shown state-of-the-art performance on various applications and has
recently emerged as a promising tool for surrogate modeling of partial differential equations …

Neural operator learning models have emerged as very effective surrogates in data-driven
methods for partial differential equations (PDEs) across different applications from …

Solving partial differential equations (PDEs) using a data-driven approach has become
increasingly common. The recent development of the operator learning paradigm has …

Numerical simulations are computationally demanding in three-dimensional (3D) settings
but they are often required to accurately represent physical phenomena. Neural operators …

Scientific Machine Learning (SciML) has advanced recently across many different areas in
computational science and engineering. The objective is to integrate data and physics …

Scientific machine learning (SciML) has advanced recently across many different areas in
computational science and engineering. The objective is to integrate data and physics …

Neural operators have emerged as a powerful tool for learning the mapping between infinite-
dimensional parameter and solution spaces of partial differential equations (PDEs). In this …

Developing fast surrogates for Partial Differential Equations (PDEs) will accelerate design
and optimization in almost all scientific and engineering applications. Neural networks have …

Neural networks have shown promising potential in accelerating the numerical simulation of
systems governed by partial differential equations (PDEs). Different from many existing …

Neural operators, as an efficient surrogate model for learning the solutions of PDEs, have
received extensive attention in the field of scientific machine learning. Among them, attention …