Research

Computational Mathematics - Nektar++

- The goal of this
effort is the development (theoretical and algorithmic) and
implementation of high-order finite element methods.

- Collaborators:
- Spencer Sherwin
(Imperial College London, United Kingdom)

- Chris Cantwell
(Imperial College London, United Kingdom)

- David Moxey
(University of Exeter, United Kingdom)

Computational Modeling and Simulation of Biological Systems

- The goal of this
effort is to bring together computational scientists and mathematical modelers to solve a multifaceted multiscale problem in human physiology. Our research goal is to cross-validate the
continuum model of the PAC cascade against a fully-realized discrete model, with the long-term
goal being the validation of these models against experimental data.

- Collaborators:
- Aaron Fogelson (Utah)
- Varun Shankar (Utah)

Mapping Scientific Computing Algorithms to GPUs and Intel Phi

- The goal of this
effort is the development (theoretical and algorithmic) and
implementation of different scientific computing algorithms to streaming multiprocessors.

- Collaborators:
- Martin Berzins (Utah)
- Ross Whitaker (Utah)

Uncertainty Quantification in Biomedical Problems

- The goal of this effort is to employ computational uncertainty modeling techniques in biomedical problems.
- Efforts:
- ECG Forward and Inverse Modeling
- Ion Channel Modeling
- RF-Ablation Modeling
- 4D-CT Uncertainty
Modeling

- Collaborators:
- Chris Johnson (Utah)
- Sarang
Joshi (Utah)

- Rob MacLeod (Utah)
- Tobias Preusser
(Jacobs University, Germany)

Uncertainty Quantification in Material Science Problems

- The goal of this effort is to employ computational uncertainty modeling techniques in material science problems.
- Efforts:
- Plasmonic Nanoparticle Design
- Battery Modeling
- Surrogate Surface Representations

- Collaborators:
- Dmitry Bedrov (Utah)
- Yanyan He (New Mexico Tech)
- Akil Narayan (Utah)
- Luca Dal Negro (Boston University)

Visualization Techniques for High-Order Finite Element Methods and Stochastic Finite Element Methods

- The goal of this
effort is to develop visualization techniques which exploit the
mathematical structure of high-order and stochastic finite element methods. Three specific efforts are: (1) the development and implementation of ElVis -- our high-order finite element visualizer; (2) our work on Smoothness-Increasing Accuracy-Conserving Filtering Methods for discontinuous Galerkin methods (DGM); and (3) computation and visualization of stochastic finite element data.

- Collaborators:
- Bob Haimes (MIT) - ElVis ("Element Visualizer")
- Mahsa Mirzargar (University of Miami)
- Jennifer Ryan
(University of East Anglia, UK) - SIAC Filtering

- Suresh Venkatasubramanian (Utah) - Stochastic FEM Visualization
- Dongbin Xiu (Ohio State University) - Stochastic FEM Visualization

- NSF Stochastic FEM Visualization Page (2009 - 2013)

- NSF OCI GPU-toolkit for Hamilton-Jacobi Equations (2012 - 2015)

- ARO High-Order Finite Element Visualizer - ElVis (2007 - 2015)

- ARO High-Order In Situ Visualization (2016 - 2018)

Application of Formal Methods to High-Performance Computing (2006 - 2009)

- The goal of this effort is to apply formal methods to the debugging and optimizing MPI libraries and MPI-based high-performance computing codes.
- Collaborators:
- Ganesh Gopalakrishnan
(Utah) - Utah Gauss Group

Last Updated: 06/08/17