Youssef Mesri

Professor

Discipline(s): Applied Mathematics, Computer Science, Energy, Thermal Sciences, Fluid Mechanics, Solid Mechanics

Keywords

Team

CFL - Calcul Intensif et Mécanique des Fluides

Biography

Youssef Mesri is a researcher whose work focuses on the development of advanced numerical methods for solving complex physical problems, particularly in the fields of fluid mechanics, high-performance computing (HPC), and artificial intelligence (AI). His research addresses major topics such as partial differential equations (PDEs), anisotropic mesh adaptation, and energy optimization of computational systems. He has contributed to the development of innovative models, such as *Compositional Neural Operators* (CompNO), aimed at improving the efficiency and interpretability of numerical solutions for parametric PDEs. His work also explores the interaction between software frameworks and energy management techniques, as well as the application of deep learning to the analysis of data from computational fluid dynamics (CFD). His expertise includes the development of flexible and portable tools for energy profiling, such as EA2P, and the integration of multiscale variational methods for accurate and adaptive simulations.

Publication(s)

2026
CompNO: A Novel Foundation Model Approach for Solving Partial Differential Equations DOI : 10.3390/app16020972
2026
SOFIA: A Python Library for High-Quality 2D Triangular Mesh Adaptation DOI : 10.21105/joss.09729
2026
InfinityEBSD : Metrics-guided infinite-size EBSD map generation with diffusion models DOI : 10.1016/j.actamat.2026.122240
2026
Compositional Neural Operators for Multi-Dimensional Fluid Dynamics DOI : 10.48550/arXiv.2605.11691
2025
A Framework for Analytical Performance and Energy Prediction of DL Training on GPUs DOI : 10.1109/SBAC-PAD66369.2025.00028
2025
Benchmark-Based Study of CPU/GPU Power-Related Features Through JAX and TensorFlow DOI : 10.1109/ACCESS.2025.3625414
2024
Graph Neural Networks for Mesh Generation and Adaptation in Structural and Fluid Mechanics DOI : 10.3390/math12182933
2024
Power Consumption in HPC-AI Systems DOI : 10.1007/978-3-031-78698-3_6
2024
Identification of vortex in unstructured mesh with graph neural networks DOI : 10.1016/j.compfluid.2023.106104
2024
A Flexible Operational Framework for Energy Profiling of Programs DOI : 10.1109/SBAC-PADW64858.2024.00014
2023
A graph neural network-based framework to identify flow phenomena on unstructured meshes DOI : 10.1063/5.0156975
2023
Power Consumption in HPC-AI Systems DOI : 10.1007/978-3-031-78698-3_6
2023
Energy concerns with hpc systems and applications DOI : 10.48550/arXiv.2309.08615
2022
Learning by neural networks under physical constraints for simulation in fluid mechanics DOI : 10.1016/j.compfluid.2022.105632
2022
High-performance 3D Unstructured Mesh Deformation Using Rank Structured Matrix Computations DOI : 10.1145/3512756
2020
A Variational Multi-Scale Anisotropic Mesh Adaptation Scheme for Aerothermal Problems DOI : 10.1007/978-3-030-30705-9_18
2020
A ‘R-to-H’ Mesh Adaptation Approach for Moving Immersed Complex Geometries Using Parallel Computers DOI : 10.1080/10618562.2020.1783441
2019
Conservative and adaptive level-set method for the simulation of two-fluid flows DOI : 10.1016/j.compfluid.2019.06.022
2019
A versatile immersed surface-to-surface method for radiation exchange: Implementation and validation DOI : 10.1016/j.ijheatmasstransfer.2019.01.051
2018
Adaptive stopping criterion for iterative linear solvers combined with anisotropic mesh adaptation, application to convection-dominated problems DOI : 10.1016/j.cma.2018.06.025
2018
Parallel and adaptive VMS finite elements formulation for aerothermal problems DOI : 10.1016/j.compfluid.2018.03.077
2018
Variational Multiscale error estimator for anisotropic adaptive fluid mechanic simulations: Application to convection–diffusion problems DOI : 10.1016/j.cma.2017.11.019
2018
Aerothermal impingement jet flow simulations using anisotropic multiscale mesh adaptation DOI : 10.2514/6.2018-2898
2018
Anisotropic adaptive stabilized finite element solver for RANS models DOI : 10.1002/fld.4475
2018
Interpolation with restrictions in an anisotropic adaptive finite element framework DOI : 10.1016/j.finel.2017.11.011
2018
Stabilized mixed finite element method for the M1 radiation model DOI : 10.1016/j.cma.2018.01.046
2017
Anisotropic boundary layer mesh generation for immersed complex geometries DOI : 10.1007/s00366-016-0469-7
2016
On optimal simplicial 3D meshes for minimizing the Hessian-based errors DOI : 10.1016/j.apnum.2016.07.007
2016
High fidelity anisotropic adaptive variational multiscale method for multiphase flows with surface tension DOI : 10.1016/j.cma.2016.04.014
2016
Adaptive variational multiscale method for bingham flows DOI : 10.1016/j.compfluid.2016.08.011
2016
Unified adaptive Variational MultiScale method for two phase compressible-incompressible flows DOI : 10.1016/j.cma.2016.05.022
2016
Unified formulation for modeling heat and fluid flow in complex real industrial equipment DOI : 10.1016/j.compfluid.2016.05.007
2015
Immersed methods and parallel anisotropic mesh adaptation to simplify the simulation setup using the cloud
2013
Parallel adaptive mesh refinement for capturing front displacements: Application to thermal EOR processes DOI : 10.2118/166058-ms
2012
Automatic coarsening of three dimensional anisotropic unstructured meshes for multigrid applications DOI : 10.1016/j.amc.2012.04.014
2011
Hierarchical adaptive multi-mesh partitioning algorithm on heterogeneous systems DOI : 10.1007/978-3-642-14438-7_32
2009
Advanced parallel computing in material forming with CIMLib DOI : 10.3166/ejcm.18.669-694
2008
Multimodel design strategies applied to sonic boom reduction DOI : 10.3166/REMN.17.245-269
2008
Dynamic parallel adaption for three dimensional unstructured meshes: Application to interface tracking DOI : 10.1007/978-3-540-87921-3_12
2008
Continuous mesh adaptation models for CFD

Teaching

ATHENS - MP06 - Nonlinear Computational Mechanics

Lecturer

Basic Material Models: Material modeling, including rheology, plasticity criteria, incremental theory of plasticity, 3D plastic flow, and basic hardening rules. Identification procedures, inverse problems. Advanced constitutive equations: cyclic and complex loading, damage models, models for thermomechanical loading, foams and cellular systems, hyperelasticity, polymeric materials. Finite element formulation: basic introduction to the method for thermal and mechanical applications. Newton’s method, element assembly, tangent matrix. Integration of constitutive equations, implicit algorithms. Nonlinear geometric and contact analysis, stabilization methods. Stability problems. Localization processes. Mesh adaptation. Coupled problems (thermal-metallurgical-mechanical interactions).

Digital Engineering of Complex Systems (IDSC) track

Lecturer

Over the past decade, artificial intelligence (AI) has profoundly transformed the industrial world by revolutionizing the ways in which systems are designed, optimized, and operated. One of the most striking aspects of this evolution is the digital modeling of complex systems. Traditionally based on methods with a strong physical or multiphysical focus, this field now benefits from advances in artificial intelligence, particularly through machine learning and deep learning algorithms. The Digital Engineering of Complex Systems track at Mines Paris aims to provide students with a solid, multidisciplinary foundation in AI, enabling them to gain a deep understanding of its applications and apply them to real-world industrial challenges. The disciplines studied in the specialization are: machine learning and its applications in engineering, computer vision, and high-performance computing. The specialization’s curriculum is structured around three weeks of coursework, supplemented by two weeks of a Data Challenge and a one-week field trip. More information is available on the track’s webpage: https://bruno-figliuzzi.github.io/Option/

Data, Optimization & Generative AI (Research Quarter)

Course Director

Simulation numérique en physique par la méthode des éléments finis

PhD supervision

2026 Development of adaptive reinforcement learning methods in multi-profile simulated environments MARTIN AHUALLI Theodoro
2024 AI-driven generation of microstructures from limited data LABADY Sterley
2024 Development of foundation models for fluid flow simulation. HMIDA Hamda
2022 High-performance, energy-efficient artificial intelligence: from measurement and modeling to multi-objective scheduling NANA TCHAKOUTE Roblex
2021 Probabilistic linear algebra strategies for precision-controlled structural mechanics simulation. BADER Wael
2021 Improving confidence in CFD results through deep learning WANG Lianfa
2017 Optimal resolution of iterative solvers with anisotropic adaptive meshing MANZINALI Gabriel
2016 Adaptive variational finite element formulation and massively parallel computation for industrial aerothermal analysis. BAZILE Alban
2016 Advanced numerical methods for simulating industrial quenching processes BAHBAH Chahrazade
2014 Modélisation du rayonnement thermique en immersion de volume Quentin Schmid
2014 Génération et adaptation de maillage volume-couche limite dynamique pour les écoulements turbulents autour de géométries complexes Laure Billon