Research statement

Frédéric Magoulès and his collaborators develop mathematical models, scalable numerical methods, and high-performance scientific software for reliable scientific and engineering simulation.

His research addresses partial differential equations, large-scale algebraic systems, and coupled multiphysics problems. It combines numerical analysis with finite-element discretizations, Krylov and gradient-based solvers, domain decomposition methods, preconditioning techniques, asynchronous iterative algorithms, and parallel and distributed computing. These methods support applications in computational mechanics, computational acoustics, energy systems, and digital twins. His work in scientific machine learning combines numerical solvers and mathematical models with physics-informed learning, graph-based models, and data-driven prediction. The overarching objective is to bridge mathematical analysis, numerical algorithms, and data-driven modelling in scalable and verifiable scientific simulation.

Research areas

Numerical Analysis · Scientific Computing · Scientific Machine Learning · High-Performance Computing · Parallel and Distributed Computing · Domain Decomposition Methods

Biosketch

Frédéric Magoulès is Full Professor of Applied Mathematics and Scientific Computing at CentraleSupélec, Université Paris-Saclay, France. He joined École Centrale Paris as Professor in 2006 and has been Professor at CentraleSupélec since 2015. Previously, he was Associate Professor and Assistant Professor at Université Henri Poincaré in Nancy, following a Temporary Assistant Professor appointment at Université Pierre et Marie Curie in Paris. He received the French Habilitation to Direct Research in Applied Mathematics from Université Pierre et Marie Curie in 2005 and a Ph.D. in Applied Mathematics from the same university in 2000, after graduate studies in numerical analysis and applied mathematics.

His research leadership includes the Transvalor S.A. Academic Chair in Artificial Intelligence Applied to the Numerical Simulation of Material Forming Processes at CentraleSupélec, of which he is Co-Inaugural Holder (2022–2026), and the SAP Academic Chair in Business Intelligence at École Centrale Paris, of which he was Inaugural Holder (2008–2013). He also directed the Centre of Integrated Engineering for Sustainable Society within the Groupe des Écoles Centrales from 2010 to 2014. He has served as Principal Investigator or Co-Principal Investigator on numerous national, European, and industrial research projects. These projects and academic chairs have supported scientific software development and technology transfer with academic and industrial partners. He has supervised more than twenty doctoral researchers and numerous postdoctoral researchers, and has mentored hundreds of Master's students.

His international academic profile includes visiting, guest, distinguished, and honorary professorships, as well as research appointments, at universities across Europe, Asia, and North America. He has also delivered invited lectures and advanced courses on domain decomposition, parallel scientific computing, asynchronous methods, and high-performance computing. He is a Fellow of the Institute of Mathematics and its Applications (IMA, United Kingdom) and a Fellow of the British Computer Society (BCS, United Kingdom). His distinctions include the IBM Faculty Award, the NVIDIA CUDA Research Center Award, the Microsoft Research Academic Award, the NVIDIA CUDA Teaching Center Award, and the IBM Champion distinction.

His editorial service has included roles on the editorial boards of Numerical Algorithms, Advances in Engineering Software, Engineering Applications of Computational Fluid Mechanics, and the International Journal of Computer Mathematics, as well as Associate Editor roles for Frontiers of Computer Science and the Journal of Algorithms and Computational Technology. He has also been Series Editor for Wiley–ISTE's Computer Engineering series and Editor-in-Chief of Chapman & Hall/CRC's Series on Numerical Analysis and Scientific Computing, and has served as guest editor for numerous special issues of international scientific journals. His scholarly output comprises more than three hundred scientific publications, including more than one hundred peer-reviewed journal articles, spanning numerical analysis, scientific computing, parallel and distributed computing, and physics-informed machine learning. He is the author or co-author of numerous books on parallel scientific computing, domain decomposition, computational methods, and machine learning for energy analysis, and has edited several scientific volumes.