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   Computational Science and Engineering Laboratory
"To address critical material
needs through innovative computational 
research, education & outreach"
Doctoral & MS dissertations supervised Archival Journal Publications Lectures & Colloquia Books Edited Refereed Conference Publications Other Conference Presentations Technical Reports
Positions Available Suggested Curriculum for Doctoral Studies
    

Current Projects

Using Relative Entropy as a Model Selection Tool for Improved Ground State Predictions in Alloys

Quantifying Uncertainties in Alloy Properties Predicted with the Cluster Expansion Method

Predicting Low Thermal Conductivity Si-Ge Nanowires with a Novel Cluster Expansion Approach

Infinite mixture of Gaussian Processes and Variational Inference for Uncertainty Propagation

Uncertainty propagation in multiscale/multiphysics materials system

Stochastic multiscale modeling of two-phase super-alloys for aircraft engine disks

Microstructure uncertainty and its progapation in multiphysics models of nuclear materials

Multiscale design of materials for extremal properties (from ab initio to continuum)

Probabilistic graphical model approach to stochastic multiscale modeling

Data-driven non-linear model reduction (manifold learning) for input and output models in stochastic PDEs

Sparse kernel approach and relevance vector machines for the solution of SPDEs in high dimensions

Bayesian inference, inverse problems and multiscale estimation with applications to non-destructive testing

Fluid flow and transport phenomena in random heterogeneous media, geological, environmental and energy applications

Multiscale models of microstructure evolution using level set and phase field methods