• Developing Domain-Constrained Representations for Stochastic Multiscale Methods

    Constructing new models accounting for microstructural complexity to advance high-fidelity stochastic multiscale methods

  • Combining Learning Techniques with Stochastic Modeling

    Combining probabilistic learning with stochastic modeling to enhance experimental and simulation datasets

  • Stochastic Modeling for Fracture

    Investigating stochastic models and identification procedures for brittle fracture (collaboration with T. Hu and J. Dolbow)

Lab Group of Dr. Johann Guilleminot

Welcome to the Uncertainty Quantification in Computational Mechanics Group 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 models endowed with 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