196 citations · 296 across the 12 of their papers we have counts for
12 papers
Efficient Computation of Tucker Decomposition for Streaming Scientific Data Compression
Saibal De, Zitong Li, Hemanth Kolla +1
The Tucker decomposition, an extension of singular value decomposition for higher-order tensors, is a useful tool in analysis and compression of large-scale scientific data. While…
Streaming Generalized Canonical Polyadic Tensor Decompositions
Eric Phipps, Nick Johnson, Tamara G. Kolda
In this paper, we develop a method which we call OnlineGCP for computing the Generalized Canonical Polyadic (GCP) tensor decomposition of streaming data. GCP differs from tradition…
Solvers and precondtioners based on Gauss-Seidel and Jacobi algorithms for non-symmetric stochastic Galerkin system of equations
Ramakrishna Tipireddy, Eric T. Phipps, Roger G. Ghanem
In this work, solvers and preconditioners based on Gauss-Seidel and Jacobi algorithms are explored for stochastic Galerkin discretization of partial differential equations (PDEs) w…
Software for Sparse Tensor Decomposition on Emerging Computing Architectures
Eric Phipps, Tamara G. Kolda
In this paper, we develop software for decomposing sparse tensors that is portable to and performant on a variety of multicore, manycore, and GPU computing architectures. The resul…
Surrogate-based Ensemble Grouping Strategies for Embedded Sampling-based Uncertainty Quantification
Marta D'Elia, Eric Phipps, Ahmad Rushdi +1
The embedded ensemble propagation approach introduced in [49] has been demonstrated to be a powerful means of reducing the computational cost of sampling-based uncertainty quantifi…
Embedded Ensemble Propagation for Improving Performance, Portability and Scalability of Uncertainty Quantification on Emerging Computational Architectures
E. Phipps, M. D'Elia, H. C. Edwards +3
Quantifying simulation uncertainties is a critical component of rigorous predictive simulation. A key component of this is forward propagation of uncertainties in simulation input…
Automating embedded analysis capabilities and managing software complexity in multiphysics simulation part II: application to partial differential equations
Roger P. Pawlowski, Eric T. Phipps, Andrew G. Salinger +3
A template-based generic programming approach was presented in a previous paper that separates the development effort of programming a physical model from that of computing additio…
Efficient Expression Templates for Operator Overloading-based Automatic Differentiation
Eric Phipps, Roger Pawlowski
Expression templates are a well-known set of techniques for improving the efficiency of operator overloading-based forward mode automatic differentiation schemes in the C++ program…
Automating embedded analysis capabilities and managing software complexity in multiphysics simulation part I: template-based generic programming
Roger P. Pawlowski, Eric T. Phipps, Andrew G. Salinger
An approach for incorporating embedded simulation and analysis capabilities in complex simulation codes through template-based generic programming is presented. This approach relie…
Hierarchical Schur complement preconditioner for the stochastic Galerkin finite element methods
Bedřich Sousedík, Roger G. Ghanem, Eric T. Phipps
Use of the stochastic Galerkin finite element methods leads to large systems of linear equations obtained by the discretization of tensor product solution spaces along their spatia…
A Lanczos Method for Approximating Composite Functions
Paul G. Constantine, Eric T. Phipps
We seek to approximate a composite function h(x) = g(f(x)) with a global polynomial. The standard approach chooses points x in the domain of f and computes h(x) at each point, whic…
Sparse Pseudospectral Approximation Method
Paul G. Constantine, Michael S. Eldred, Eric T. Phipps
Multivariate global polynomial approximations - such as polynomial chaos or stochastic collocation methods - are now in widespread use for sensitivity analysis and uncertainty quan…