Physics-Informed Neural Networks (PINN) are neural networks encoding the problem
governing equations, such as Partial Differential Equations (PDE), as a part of the neural …

We introduce a new class of hybrid preconditioners for solving parametric linear systems of
equations. The proposed preconditioners are constructed by hybridizing the deep operator …

Solving large-scale sparse linear systems is essential in fields like mathematics, science,
and engineering. Traditional numerical solvers, mainly based on the Krylov subspace …

Large sparse linear algebraic systems can be found in a variety of scientific and engineering
fields and many scientists strive to solve them in an efficient and robust manner. In this …

We design and develop a new Particle-in-Cell (PIC) method for plasma simulations using
Deep-Learning (DL) to calculate the electric field from the electron phase space. We train a …

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 …

Efficiently solving sparse linear algebraic equations is an important research topic of
numerical simulation. Commonly used approaches include direct methods and iterative …

In computational fluid dynamics, the Boussinesq approximation is a popular model for the
numerical simulation of natural convection problems. Although using the Boussinesq …

Data Assimilation is the process in which we improve the representation of the state of a
physical system by combining information coming from a numerical model, real-world …

Depuis l'ère industrielle, les villes sont impactées par la pollution de l'air du fait de la densité
de l'industrie, de la circulation de véhicules et de la densité d'appareils de chauffage à …