Probabilistic and High Order Inference, Computation, Estimation, and Simulation

Vision of Pho-Ices group

The uniqueness of Pho-Ices group is to bring together advances from stochastic programming, probability theory, parallel computing, high-order discretization methods, and mathematical analysis to conduct research on: 1) model order reduction, 2) PDE-constrained optimization, 3) high-order finite element methods, 4) parallel computing, 5) statistical inverse problems, 6) uncertainty quantification, 7) data reduction methods, and 8) machine learning.

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Projects

1) CAREER: Scalable Approaches for Large-Scale Data-driven Bayesian Inverse Problems in High Dimensional Parameter Spaces
NSF

2) CDS&E:Collaborative Research: Strategies for Managing Data in Uncertainty Quantification at Extreme Scales
NSF
Hari Sundar (Co-PI), University of Utah, Salt Lake

3) mOSaIc: Atmosphere-Ocean-Solid Earth Coupling: Exploring Innovative Tools to Monitor the Oceans
UT-Portugal
Susana Custodio (PI), University of Lisbon, Graca Silveira (Co-PI) University of Lisbon

4) Models with multiple levels of fidelity, tractability, and computational cost for nuclear weapon radiation effects
DTRA
Jean Ragusa (PI), Texas A&M, Marvin Adams (Co-PI), Texas A&M, Jim Morel (Co-PI), Texas A&M

5) Tokamak Disruption Simulation
DOE
John Shadid (PI), SNL, Xianzhu Tang (PI), LANL

6) Large-scale Inverse Problems and UQ for Reservoir Modeling
ExxonMobil-UTEI
Omar Ghattas (PI), GEO
Clint Dawson (Co-PI), EM
George Biros (Co-PI), ME

7) A Scalable High-Order Discontinuous Finite Element Framework for PDEs: with Application to Geophysical Fluid Flows
NSF

Probabilistic and High Order Inference, Computation, Estimation, and Simulation

Tan Bui-Thanh

Krishnanunni Chandradath Girija

Jau-Uei Chen

Tri Do

Wesley Lao

Hai Nguyen

Karissa Vail