Roger G. Ghanem

According to our database1, Roger G. Ghanem authored at least 32 papers between 2004 and 2019.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.



In proceedings 
PhD thesis 





Compressive sensing adaptation for polynomial chaos expansions.
J. Comput. Physics, 2019

Entropy-based closure for probabilistic learning on manifolds.
J. Comput. Physics, 2019

The Stochastic Quasi-chemical Model for Bacterial Growth: Variational Bayesian Parameter Update.
J. Nonlinear Science, 2018

Reduced Wiener Chaos representation of random fields via basis adaptation and projection.
J. Comput. Physics, 2017

Polynomial chaos representation of databases on manifolds.
J. Comput. Physics, 2017

Homogeneous chaos basis adaptation for design optimization under uncertainty: Application to the oil well placement problem.
AI EDAM, 2017

Uncertainty quantification for engineering design.
AI EDAM, 2017

Uncertainty quantification for engineering design.
AI EDAM, 2017

Data-driven probability concentration and sampling on manifold.
J. Comput. Physics, 2016

Probabilistic Approach to NASA Langley Research Center Multidisciplinary Uncertainty Quantification Challenge Problem.
J. Aerospace Inf. Sys., 2015

Hierarchical Schur complement preconditioner for the stochastic Galerkin finite element methods : Dedicated to Professor Ivo Marek on the occasion of his 80th birthday.
Numerical Lin. Alg. with Applic., 2014

Basis adaptation in homogeneous chaos spaces.
J. Comput. Physics, 2014

Multiscale Stochastic Representation in High-Dimensional Data Using Gaussian Processes with Implicit Diffusion Metrics.
Proceedings of the Dynamic Data-Driven Environmental Systems Science, 2014

Simple Urban Simulation Atop Complicated Models: Multi-Scale Equation-Free Computing of Sprawl Using Geographic Automata.
Entropy, 2013

Accelerating agent-based computation of complex urban systems.
International Journal of Geographical Information Science, 2012

Identification of Bayesian posteriors for coefficients of chaos expansions.
J. Comput. Physics, 2010

Efficient Monte Carlo computation of Fisher information matrix using prior information.
Computational Statistics & Data Analysis, 2010

A Bounded Random Matrix Approach for Stochastic Upscaling.
Multiscale Modeling & Simulation, 2009

Polynomial chaos representation of spatio-temporal random fields from experimental measurements.
J. Comput. Physics, 2009

Asymptotic Sampling Distribution for Polynomial Chaos Representation from Data: A Maximum Entropy and Fisher Information Approach.
SIAM J. Scientific Computing, 2008

Multi-Resolution-Analysis Scheme for Uncertainty Quantification in Chemical Systems.
SIAM J. Scientific Computing, 2007

An efficient calculation of Fisher information matrix: Monte Carlo approach using prior information.
Proceedings of the 46th IEEE Conference on Decision and Control, 2007

On the construction and analysis of stochastic models: Characterization and propagation of the errors associated with limited data.
J. Comput. Physics, 2006

Ultrasound Monitoring of Tissue Ablation Via Deformation Model and Shape Priors.
Proceedings of the Medical Image Computing and Computer-Assisted Intervention, 2006

Domain Decompostion Of Stochastic PDEs and its Parallel.
Proceedings of the 20th Annual International Symposium on High Performance Computing Systems and Applications (HPCS 2006), 2006

Error Estimation in the Spatial Discretization of Multiscale Bridging Models.
Multiscale Modeling & Simulation, 2005

A Multiscale Data Assimilation with the Ensemble Kalman Filter.
Multiscale Modeling & Simulation, 2005

An equation-free, multiscale approach to uncertainty quantification.
Computing in Science and Engineering, 2005

Physical Systems with Random Uncertainties: Chaos Representations with Arbitrary Probability Measure.
SIAM J. Scientific Computing, 2004

Natural Convection in a Closed Cavity under Stochastic Non-Boussinesq Conditions.
SIAM J. Scientific Computing, 2004

Special Issue on Uncertainty Quantification.
SIAM J. Scientific Computing, 2004

Numerical Challenges in the Use of Polynomial Chaos Representations for Stochastic Processes.
SIAM J. Scientific Computing, 2004