Johann Guilleminot

Paul Ruffin Scarborough Associate Professor of Engineering

Johann Guilleminot is the Paul Ruffin Scarborough Associate Professor of Engineering and an Associate Professor of Civil and Environmental Engineering 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 uncertainty quantification, 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.

Appointments and Affiliations

  • Paul Ruffin Scarborough Associate Professor of Engineering
  • Associate Professor in the Department of Civil and Environmental Engineering
  • Associate Professor in the Thomas Lord Department of Mechanical Engineering and Materials Science

Contact Information

  • Office Location: 172 Hudson Hall, Box 90287, Durham, NC 27708
  • Office Phone: +1 919 684 3537
  • Email Address: johann.guilleminot@duke.edu
  • Websites:

Education

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

Research Interests

Computational mechanics, mechanics of heterogeneous materials, molecular dynamics simulations and atomistic-to-continuum coupling, stochastic solvers, statistical inverse problem and model validation, stochastic analysis, uncertainty quantification in science and engineering

Courses Taught

  • MENG 551: Master of Engineering Internship/Project Assessment
  • MENG 550: Master of Engineering Internship/Project
  • ME 758S: Curricular Practical Training
  • ME 582: Applications in Data and Materials Science
  • ME 524: Introduction to the Finite Element Method
  • EGR 393: Research Projects in Engineering
  • COMPSCI 583: Applications in Data and Materials Science
  • CEE 780: Internship
  • CEE 628: Uncertainty Quantification in Computational Science and Engineering
  • CEE 530: Introduction to the Finite Element Method
  • CEE 421L: Matrix Structural Analysis
  • CEE 394: Research Independent Study in Civil and Environmental Engineering

Representative Publications

  • Staber, B., and J. Guilleminot. “Stochastic modeling and generation of random fields of elasticity tensors: A unified information-theoretic approach.” Comptes Rendus - Mecanique 345, no. 6 (June 1, 2017): 399–416. https://doi.org/10.1016/j.crme.2017.05.001.
  • Staber, B., and J. Guilleminot. “Stochastic modeling of the Ogden class of stored energy functions for hyperelastic materials: the compressible case.” ZAMM Zeitschrift Fur Angewandte Mathematik Und Mechanik 97, no. 3 (March 1, 2017): 273–95. https://doi.org/10.1002/zamm.201500255.
  • Staber, B., and J. Guilleminot. “Stochastic hyperelastic constitutive laws and identification procedure for soft biological tissues with intrinsic variability.” Journal of the Mechanical Behavior of Biomedical Materials 65 (January 2017): 743–52. https://doi.org/10.1016/j.jmbbm.2016.09.022.
  • Staber, B., and J. Guilleminot. “Functional approximation and projection of stored energy functions in computational homogenization of hyperelastic materials: A probabilistic perspective.” Computer Methods in Applied Mechanics and Engineering 313 (January 1, 2017): 1–27. https://doi.org/10.1016/j.cma.2016.09.019.
  • Le, T. T., J. Guilleminot, and C. Soize. “Stochastic continuum modeling of random interphases from atomistic simulations. Application to a polymer nanocomposite.” Computer Methods in Applied Mechanics and Engineering 303 (May 1, 2016): 430–49. https://doi.org/10.1016/j.cma.2015.10.006.
  • Guilleminot, J., and C. Soize. “Itô SDE-based generator for a class of non-Gaussian vector-valued random fields in uncertainty quantification.” SIAM Journal on Scientific Computing 36, no. 6 (January 1, 2014): A2763–86. https://doi.org/10.1137/130948586.