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
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 CiteLearning 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 CiteUncertainty 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 CiteRecent Grants
NRT-HDR: Harnessing AI for Autonomous Material Design
Inst. Training Prgm or CMECo-Principal Investigator · Awarded by National Science Foundation · 2020 - 2026Stochastic Modeling and Multiscale Propagation of Model-Form Uncertainties Arising in Molecular Dynamics Simulations
ResearchPrincipal Investigator · Awarded by Army Research Office · 2023 - 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