Overview
Johann Guilleminot is the Paul Ruffin Scarborough Associate Professor of Engineering and an Associate Professor of Mechanical Engineering and Materials Science at Duke University. He joined Duke on July 1, 2017.
Prior to that, he held a Maître de Conférences position in the Multiscale Modeling and Simulation Laboratory at Université Paris-Est in France.
He earned an MS (2005) and PhD (2008) in Theoretical Mechanics from the University of Lille 1 Science and Technology (France), and received his Habilitation (2014) in Mechanics from Université Paris-Est. Habilitation is the highest academic degree in France.
Dr. Guilleminot’s research focuses on probabilistic methods, computational mechanics and materials science, as well as on topics at the interface between these fields. He is particularly interested in the multiscale analysis of linear/nonlinear heterogeneous materials (including biological and engineered ones), homogenization theory, scientific machine learning, statistical inverse problems and stochastic modeling with applications for computational science and engineering.
Current Duke Appointments & Affiliations
Recent Scholarly Works
Stochastic modeling of anisotropic strength surfaces from atomistic simulations
Journal article Theoretical and Applied Fracture Mechanics · July 1, 2026 This work develops a unified framework for inferring, representing, and statistically characterizing an anisotropic strength surface directly from molecular dynamics data. Large-scale tensile loading simulations are used to generate failure data across all ... Full text CiteWhen model-form and parametric uncertainties matter: A unified stochastic representation for propagation and sensitivity analysis
Journal article Computer Methods in Applied Mechanics and Engineering · April 1, 2026 This work develops a new probabilistic framework for uncertainty quantification and sensitivity analysis in computational engineering, focusing on the case of mixed uncertainties arising from both parametric and model-form sources. We introduce a stochasti ... Full text CiteData generation with optimal experimental design for operator learning
Journal article Computer Methods in Applied Mechanics and Engineering · March 1, 2026 Partial differential equations (PDEs) are fundamental to modeling complex physical phenomena across scientific disciplines. While operator learning offers a promising alternative to conventional PDE solvers, it generally requires substantial high-fidelity ... Full text CiteRecent Grants
Stochastic Modeling and Multiscale Propagation of Model-Form Uncertainties Arising in Molecular Dynamics Simulations
ResearchPrincipal Investigator · Awarded by Army Research Office · 2023 - 2027NRT-HDR: Harnessing AI for Autonomous Material Design
Inst. Training Prgm or CMECo-Principal Investigator · Awarded by National Science Foundation · 2020 - 2026CAREER: Harnessing the Revolution in Material Processing: A Computational Stochastic Framework for Uncertainty Quantification on Optimized Geometrics
ResearchPrincipal Investigator · Awarded by National Science Foundation · 2020 - 2026View All Grants