One of the most promising applications of machine learning in computational physics is to
accelerate the solution of partial differential equations (PDEs). The key objective of machine …

Computational fluid dynamics (CFD) represents a valuable tool in the design process of built
environments, enhancing the comfort, health, energy efficiency, and safety of indoor and …

This study addresses the computational challenges in fluid flow simulations arising from
demanding computational grids, required to capture the temporal and length scales …

By following the statistics, over the last few years, the use of Artificial Intelligence conjectured
with mathematical models has increased abundantly for physical problems having thermal …

This paper focuses on the discovery of optimal flapping wing kinematics using a deep
learning surrogate model for unsteady aerodynamics and multi-objective optimisation. First …

Machine learned partial differential equation (PDE) solvers trade the reliability of standard
numerical methods for potential gains in accuracy and/or speed. The only way for a solver to …

A deep learning surrogate for the direct numerical temporal prediction of two-dimensional
acoustic waves propagation and scattering with obstacles is developed through an …

In this study, the modeling of the compressible pressure field on the RAE 2822 airfoil using
deep learning (DL) is investigated. The objective is to generate, at low cost, the complete …

This paper presents Ψ-GNN, a novel Graph Neural Network (GNN) approach for solving the
ubiquitous Poisson PDE problems on general unstructured meshes with mixed boundary …

Modelling the flame response of turbulent flames via data-driven approaches is challenging
due, among others, to the presence of combustion noise. Neural network methods have …