Franck Ledoux

LiHPC, Teratec

2 Rue de la Piquetterie

91680 Bruyères-le-Châtel

I am member of the LIHPC research lab and director of research at CEA, France. I work on designing and providing meshing algorithms and software for the purpose of numerical simulations. My main research interests are about: quadrilateral and hexahedral mesh generation, mesh adaptation and mesh partitioning.

In our team, we produce quadrilateral and hexahedral mesh generation for the purpose of hydrodynamics and areodynamics codes. In this context, preferred meshes are said block-structured. It is my main research topic. Today, I consider that frame-field based global parameterizations, including Polycube, are the option to follow. As a complement, with researchers of IBISC, we investigate how to update block structures using reinforcement learning techniques.

Generating meshes for numerical simulation codes raises the question of getting the “right” mesh. Trying to answer leads to investigate how to generate/adapt a mesh considering data provided by the simulation code itself. It means to adapt the mesh to numerical fields and not only to a geometrical shape.

The last component of my research is mesh partitioning. It is a key feature for running HPC simulation codes efficiently. Our specificity is that we partition meshes and not graphs and we deal both with architecture constraints (MPI process, CPU threads, GPU threads) and code requirements (multi-stages physics, load balancing on the fly).

news

Aug 24, 2026	An extended version of the survey about AI and meshing, which was presented at the SIAM International Meshing Roundtable in March 2026, has been accepted for publication in Engineering with Computers (see here). Many thanks to Steven Owen, Nathan Brown, Nikos Chrisochoides, Rao Garimella, Xianfeng Gu, Na Lei, Roshan Quadros, Navamita Ray, Nick Winovich and Yongjie Jessica Zhang for their hard work gathering meaningful data and knowledge.
Aug 09, 2026	The article entitled “Automatic curved block structure and hexahedral mesh generation for hypersonic CFD” and written with Claire Roche, Jérôme Breil, Simon Calderan, and Thierry Hocquellet, has been accepted in the international journal Computer Methods in Applied Mechanics and Engineering (see https://doi.org/https://doi.org/10.1016/j.cma.2026.119209). It is a synthetic view of Claire’s Phd work, that you can access here. Congrats to her!

Aug 24, 2026

An extended version of the survey about AI and meshing, which was presented at the SIAM International Meshing Roundtable in March 2026, has been accepted for publication in Engineering with Computers (see here). Many thanks to Steven Owen, Nathan Brown, Nikos Chrisochoides, Rao Garimella, Xianfeng Gu, Na Lei, Roshan Quadros, Navamita Ray, Nick Winovich and Yongjie Jessica Zhang for their hard work gathering meaningful data and knowledge.

Aug 09, 2026

The article entitled “Automatic curved block structure and hexahedral mesh generation for hypersonic CFD” and written with Claire Roche, Jérôme Breil, Simon Calderan, and Thierry Hocquellet, has been accepted in the international journal Computer Methods in Applied Mechanics and Engineering (see https://doi.org/https://doi.org/10.1016/j.cma.2026.119209). It is a synthetic view of Claire’s Phd work, that you can access here. Congrats to her!

selected publications

A survey of AI methods for geometry preparation and mesh generation in engineering simulation

Steven Owen, Nathan Brown, Nikos Chrisochoides, and 8 more authors

Engineering with Computers, 2026

Abs DOI PDF

Artificial intelligence is beginning to reduce the manual effort in the CAD-to-mesh pipeline. Written for meshing and geometry practitioners with limited AI background, this survey organizes recent work by workflow step. We cover part classification and segmentation, mesh quality prediction, and defeaturing. We review AI guidance for unstructured meshing, block-structured meshing in 2D and 3D, and volumetric parameterization, including reconstruction from implicit or sampled geometry. We also discuss parallel mesh generation and scripting automation via reinforcement learning and large language models. Across these topics, AI complements established geometry and meshing algorithms rather than replacing them. We conclude with practical lessons and open challenges in data, benchmarks, and trustworthy integration.
Automatic curved block structure and hexahedral mesh generation for hypersonic CFD

Claire Roche, Jérôme Breil, Simon Calderan, and 2 more authors

Computer Methods in Applied Mechanics and Engineering, 2026

Abs DOI PDF

Hexahedral block-structured meshes are known as difficult to generate and are usually created manually using specialized interactive software. For industrial complex geometries, this process is highly time-consuming and hinders the numerical simulation process. Currently, automatic hexahedral mesh generation is a complex and open area of research. While some techniques show promise, they still fail to provide a suitable input for numerical simulations. In this work, we present a method for generating curved block-structured hexahedral meshes of the fluid domain surrounding vehicles for use in flow simulations of re-entry vehicles. This method is decomposed as follows: First, a linear block structure is extruded from the initial vehicle surface discretization using an advancing front process. Once this linear block structure has been generated, the block structure is curved a posteriori using Bézier polynomials. This improves the representation of the vehicle surface. Next, we generate a linear mesh from the curved blocks under constraints. The quality of the generated mesh is evaluated in two ways: First, we analyze it using purely geometrical criteria and then we use obtained meshed for numerical simulations of flows around re-entry vehicles.