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 …

Abstract Machine learning (ML) and artificial intelligence have proven to be an invaluable
tool in tackling a vast array of scientific, engineering, and societal problems. The main …

The resolution of the Poisson equation is usually one of the most computationally intensive
steps for incompressible fluid solvers. Lately, DeepLearning, and especially convolutional …

Machine learning has had an enormous impact in many scientific disciplines. It has also
attracted significant interest in the field of low-temperature plasma (LTP) modeling and …

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 …

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 …

Turbulence in fluids, gases, and plasmas remains an open problem of both practical and
fundamental importance. Its irreducible complexity usually cannot be tackled …

Iterative methods are ubiquitous in large-scale scientific computing applications, and a
number of approaches based on meta-learning have been recently proposed to accelerate …

In the field of magnetic confinement plasma control, the accurate feedback of plasma
position and shape primarily relies on calculations derived from magnetic measurements …

This article presents a convolutional neural network (CNN)-based deep-learning (DL)
model, inspired from UNet with a series of encoder and decoder units with skip connections …