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Johann Guilleminot

Paul Ruffin Scarborough Associate Professor of Engineering
Thomas Lord Department of Mechanical Engineering and Materials Science
172 Hudson Hall, Box 90287, Durham, NC 27708
172 Hudson Hall, Box 90287, Durham, NC 27708

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 Appointments & Affiliations


Paul Ruffin Scarborough Associate Professor of Engineering · 2023 - Present Thomas Lord Department of Mechanical Engineering and Materials Science, Pratt School of Engineering
Associate Professor in the Thomas Lord Department of Mechanical Engineering and Materials Science · 2024 - Present Thomas Lord Department of Mechanical Engineering and Materials Science, Pratt School of Engineering
Associate Professor in the Department of Civil and Environmental Engineering · 2024 - Present Civil and Environmental Engineering, Pratt School of Engineering

Recent Publications


Enhancing robustness in machine-learning-accelerated molecular dynamics: A multi-model nonparametric probabilistic approach

Journal Article Mechanics of Materials · March 1, 2025 In this work, we present a system-agnostic probabilistic framework to quantify model-form uncertainties in molecular dynamics (MD) simulations based on machine-learned (ML) interatomic potentials. Such uncertainties arise from the design and selection of M ... Full text Cite

Learning latent space dynamics with model-form uncertainties: A stochastic reduced-order modeling approach

Journal Article Computer Methods in Applied Mechanics and Engineering · February 15, 2025 This paper presents a probabilistic approach to represent and quantify model-form uncertainties in the reduced-order modeling of complex systems using operator inference techniques. Such uncertainties can arise in the selection of an appropriate state–spac ... Full text Cite

Uncertainty quantification of acoustic metamaterial bandgaps with stochastic material properties and geometric defects

Journal Article Computers and Structures · December 1, 2024 Acoustic metamaterials are a subject of increasing study and utility. Through designed combinations of geometries with material properties, acoustic metamaterials can be built to arbitrarily manipulate acoustic waves for various applications. Despite the t ... Full text Cite
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Recent Grants


NRT-HDR: Harnessing AI for Autonomous Material Design

Inst. Training Prgm or CMECo-Principal Investigator · Awarded by National Science Foundation · 2020 - 2026

Stochastic Modeling and Multiscale Propagation of Model-Form Uncertainties Arising in Molecular Dynamics Simulations

ResearchPrincipal Investigator · Awarded by Army Research Office · 2023 - 2026

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Education, Training & Certifications


Lille University of Science and Technology (France) · 2008 Ph.D.
Lille University of Science and Technology (France) · 2005 M.S.