output
20222024
most citedPhysics-Informed Graph-Mesh Networks for PDEs: A hybrid approach for complex problems

7 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

collaborating institutions

5 papers

math.NA2024★ 7 cited

Physics-Informed Graph-Mesh Networks for PDEs: A hybrid approach for complex problems

Marien Chenaud, Frédéric Magoulès, José Alves

The recent rise of deep learning has led to numerous applications, including solving partial differential equations using Physics-Informed Neural Networks. This approach has proven…

cs.LG2024

Gaussian process interpolation with conformal prediction: methods and comparative analysis

Aurélien Pion, Emmanuel Vazquez

This article advocates the use of conformal prediction (CP) methods for Gaussian process (GP) interpolation to enhance the calibration of prediction intervals. We begin by illustra…

cs.DC2024

Fortran... ok, and what's next?

Vincent Magnin, José Alves, Antoine Arnoud +2

Modern Fortran is a standardized language that includes object-oriented and parallel programming paradigms. The Fortran-lang community, created at the end of 2019, is actively work…

cs.LG2023★ 3 cited

Physics-Informed Graph Convolutional Networks: Towards a generalized framework for complex geometries

Marien Chenaud, José Alves, Frédéric Magoulès

Since the seminal work of [9] and their Physics-Informed neural networks (PINNs), many efforts have been conducted towards solving partial differential equations (PDEs) with Deep L…

math.NA2022★ 3 cited

Graph Neural Network-based Surrogate Models for Finite Element Analysis

Meduri Venkata Shivaditya, José Alves, Francesca Bugiotti +1

Current simulation of metal forging processes use advanced finite element methods. Such methods consist of solving mathematical equations, which takes a significant amount of time…