Numerical modelling is a reliable tool for flood simulations, but accurate solutions are
computationally expensive. In recent years, researchers have explored data-driven …

This paper explores the evolution of geoscientific inquiry, tracing the progression from
traditional physics-based models to modern data-driven approaches facilitated by significant …

Abstract The Finite Element Method, an important technique in engineering, is aided by
Adaptive Mesh Refinement (AMR), which dynamically refines mesh regions to allow for a …

Mesh-based simulations play a key role when modeling complex physical systems that, in
many disciplines across science and engineering, require the solution to parametrized time …

We present a novel graph transformer framework, HAMLET, designed to address the
challenges in solving partial differential equations (PDEs) using neural networks. The …

Simulations of turbulent flows in 3D are one of the most expensive simulations in
computational fluid dynamics (CFD). Many works have been written on surrogate models to …

Engineering components must meet increasing technological demands in ever shorter
development cycles. To face these challenges, a holistic approach is essential that allows …

The rapid development of deep learning has significant implications for the advancement of
computational fluid dynamics. Currently, most pixel-grid-based deep learning methods for …

We consider using deep neural networks to solve time-dependent partial differential
equations (PDEs), where multi-scale processing is crucial for modeling complex, time …

Neural networks are emerging as a tool for scalable data-driven simulation of high-
dimensional dynamical systems, especially in settings where numerical methods are …