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Franck N’guyen

Franck N’guyen

Lecturer

Center · CMAT

Team

SIMS - Simulation des Matériaux et des Structures

Biography

Franck N’Guyen is a researcher specializing in numerical methods applied to materials mechanics and the morphological analysis of complex structures. His work focuses on the development of advanced techniques for geometric modeling and numerical simulation, particularly through the use of constrained Delaunay triangulation (CDT) applied to 2D and 3D images with high morphological complexity. His research also addresses the numerical homogenization of the mechanical and thermal properties of composite materials, integrating experimental, theoretical, and computational approaches to characterize heterogeneous microstructures, such as biocomposites reinforced with natural particles or filled polymers. A significant portion of her work focuses on the analysis of defects and their impact on mechanical behavior, combining 3D imaging tools (tomography, microscopy) with model reduction and machine learning methods to optimize predictions of material lifespan and strength under thermomechanical stresses.

Publication(s)

Teaching

Digital Engineering of Complex Systems (IDSC) track

Guest 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 lies in the numerical 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/

PhD supervision

  • 2024 Crystal plasticity modeling of the dwell fatigue behavior of the TA6V titanium alloy ABLA Widad
  • 2019 Ductile failure of a random porous medium: Numerical approach and application to weld defects CADET Clément