• Stochastic Multiscale Modeling in Lubrication

    Stochastic Multiscale Modeling in Fluid Mechanics

    In this collaborative project, we have proposed a stochastic multiscale framework for hydrodynamics lubrication.

  • Computational Mechanics and UQ for Vascular Tissues

    Stochastic Computational Mechanics for Biological Tissues

    In this project, funded by the National Science Foundation, we are investigating the construction and simulation of stochastic nonlinear constitutive laws for soft biological tissues, like arterial walls.

  • MD Simulations and UQ for Nanoscale Systems

    Coupling Molecular Dynamics Simulations with Stochastic Multiscale Models for Nanocomposites

    In this research, we have addressed the stochastic multiscale modeling of random interphases in nanocomposites. The approach heavily relies on atomistic computations, as well as on a statistical inverse problem involving stochastic homogenization.

Lab Group of Dr. Johann Guilleminot

Welcome to the Uncertainty Quantification in Computational Mechanics Group in the Department of Civil and Environmental Engineering at Duke University

Our research aims at proposing new methodologies, stochastic models and robust algorithms for Uncertainty Quantification (UQ) in Computational Mechanics, Materials Science and Mechanics of Materials. We specifically tackle the following core questions as an interdisciplinary research:

  • How to faithfully model data under both mathematical and physical constraints?
  • How to efficiently localize/propagate uncertainties in (non)linear complex models?
  • How to calibrate and validate large-scale models, given typical experimental constraints?
  • How to provide engineers with high-fidelity models endowed with probabilistic measures of uncertainties?

The proposed theoretical and computational frameworks find applications in a broad range of engineering and scientific fields or projects, such as:

  • The multiscale and multiphysics modeling of complex heterogeneous materials and systems.
  • The identification of linear or nonlinear material properties solving statistical inverse problems.
  • The modeling of (non)linear materials with non separated scales and/or uncertainties.
  • The propagation of physics-based models of uncertainties through high-dimensional models.

Some parts of this website are currently under construction: please check back for updates!

Financial support from Duke University, the National Science Foundation, the U.S. Naval Research Laboratory and Sandia National Laboratories is gratefully acknowledged.

National Science Foundation