Transformers are widely used deep learning architectures. Existing transformers are mostly
designed for sequences (texts or time series), images or videos, and graphs. This paper …

Abstract Machine learning approaches for solving partial differential equations require
learning mappings between function spaces. While convolutional or graph neural networks …

This paper explores the recent advancements in enhancing Computational Fluid Dynamics
(CFD) tasks through Machine Learning (ML) techniques. We begin by introducing …

The long runtime of high-fidelity partial differential equation (PDE) solvers makes them
unsuitable for time-critical applications. We propose to accelerate PDE solvers using …

Modeling interacting dynamical systems, such as fluid dynamics and intermolecular
interactions, is a fundamental research problem for understanding and simulating complex …

Eddy-resolving turbulence simulations are essential for understanding and controlling
complex unsteady fluid dynamics, with significant implications for engineering and scientific …

Neural operators, which can act as implicit solution operators of hidden governing
equations, have recently become popular tools for learning the responses of complex real …

Fourier neural operators (FNOs) can learn highly nonlinear mappings between function
spaces, and have recently become a popular tool for learning responses of complex …

Data assimilation is crucial in a wide range of applications, but it often faces challenges such
as high computational costs due to data dimensionality and incomplete understanding of …

Recently, Neural Fields have emerged as a powerful modelling paradigm to represent
continuous signals. In a conditional neural field, a field is represented by a latent variable …