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Pierre Kerfriden

Pierre Kerfriden

Professor

Center · CMAT

Team

SIMS - Simulation des Matériaux et des Structures

Biography

Pierre Kerfriden is a researcher specializing in numerical modeling and materials mechanics, with particular expertise in the analysis of multiscale and stochastic phenomena. His work focuses in particular on the development of advanced methods for simulating mechanical and hydraulic behavior in heterogeneous media, such as soils or composite materials. He has contributed to the development of stochastic models incorporating Gaussian random fields to represent the spatial variability of material properties, as well as to innovative approaches for local plastic correction and the acceleration of finite element simulations. His research also includes the application of machine learning techniques, such as convolutional neural networks and Gaussian processes, to optimize industrial processes or improve the prediction of stress fields in porous structures. Pierre Kerfriden is also exploring hybrid methods that combine physics and data to enhance the robustness and efficiency of numerical simulations, while incorporating the uncertainties and variabilities inherent in the systems under study.

Publication(s)

Teaching

Mechanics of Materials and Structures (PI MECAERO)

Lecturer

Experimental Mechanics

Guest Lecturer

The course has two main objectives. Drawing on examples from industry, the course first aims to help students understand the challenges involved in a mechanical testing campaign and to equip them with the tools needed to design experimental plans for the mechanical characterization of materials. With this in mind, the course will begin with a description of standardized tests to highlight their limitations, followed by a presentation of original or “non-standard” tests that are as well-instrumented as possible. To this end, part of the course will be devoted to the study of non-contact thermomechanical measurement techniques (infrared thermography and digital image correlation). In a second phase, the focus will be on establishing the link between continuum mechanics, thermomechanical behavior equations, and experimental characterization. The concepts of stress and strain analysis will be reviewed to understand the methods for optimizing behavior and damage mechanisms, which we aim to identify through the tests. The in-person component (29 hours) is structured into plenary sessions (12 hours), mini-projects (in pairs or groups of three) (15 hours), and project presentations before a panel (30 minutes). Students’ independent work (6 hours) includes: Understanding the mini-project, literature review, scientific analysis and interpretation of the results obtained, writing a summary, and preparing an oral presentation

PhD supervision

  • 2025 Identification of material models up to failure using stochastic and multi-parametric approaches MARTELLI Anthony
  • 2025 Data-driven learning of an electric vehicle battery aging model HOLLER Colin
  • 2025 Generative artificial intelligence models for metallic alloy microstructures COURTOIS Martin
  • 2023 Modeling and AI methods to estimate and predict the health states of electric vehicle batteries. VU Germain
  • 2023 Digital twinning for real-time monitoring of the health status of composite structures monitored by acoustic emissions CHEHAZI Marwa
  • 2022 Thermomechanical behavior of refractory concretes subjected to severe upward thermal shocks GUERZIZ Arij
  • 2022 Shape optimization using the level-set cutFEM method and application to the design of mechanical characterization specimens EL BACHARI Amina
  • 2021 Discover the constitutive law of nonlinear viscoelastic material using physics-informed neural networks and experimental data PISTENON Nicolas
  • 2021 A multi-scale probabilistic methodology to predict the fatigue life of porous alloys from tomographic images PALCHOUDHARY Abhishek
  • 2021 Advanced numerical simulation for understanding low points in the resilience of forged steels NGOUADJE KENKO Régis Emmanuel
  • 2021 Data-driven numerical simulations for controlling the mechanical properties of DLF composites TOUMINET Armand
  • 2019 Multi-scale digital twin and data assimilation for predicting damage in composite pressure vessels KLEBI Nesrine
  • 2019 Thermo-mechanical simulation of the Wire Arc Additive Manufacturing (WAAM) process HILAL Sami
  • 2019 Real-time prediction of mechanical stresses in welding using a metamodel library PEREIRA ALVAREZ Pablo
  • 2019 Ductile failure of a random porous medium: Numerical approach and application to weld defects CADET Clément
  • 2019 Geometric complex simplification based on data PERNEY Antoine