P
Philippe Pierre Pebay
Researcher at Sandia National Laboratories
Publications - 86
Citations - 1906
Philippe Pierre Pebay is an academic researcher from Sandia National Laboratories. The author has contributed to research in topics: Scalability & Embarrassingly parallel. The author has an hindex of 18, co-authored 85 publications receiving 1773 citations. Previous affiliations of Philippe Pierre Pebay include Princeton University & Kitware.
Papers
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Journal ArticleDOI
Numerical Challenges in the Use of Polynomial Chaos Representations for Stochastic Processes
Bert Debusschere,Habib N. Najm,Philippe Pierre Pebay,Omar M. Knio,Roger Ghanem,Olivier Le Maitre +5 more
TL;DR: This paper gives an overview of the use of polynomial chaos (PC) expansions to represent stochastic processes in numerical simulations and finds that the integration method offers a robust and accurate approach for evaluating nonpolynomial functions, even when very high-order information is present in the PC expansions.
Proceedings ArticleDOI
Combining in-situ and in-transit processing to enable extreme-scale scientific analysis
Janine C. Bennett,Hasan Abbasi,Peer-Timo Bremer,Ray Grout,Attila Gyulassy,Tong Jin,Scott Klasky,Hemanth Kolla,Manish Parashar,Valerio Pascucci,Philippe Pierre Pebay,David C. Thompson,Hongfeng Yu,Fan Zhang,Jacqueline H. Chen +14 more
TL;DR: The lightweight, flexible framework allows scientists dealing with the data deluge at extreme scale to perform analyses at increased temporal resolutions, mitigate I/O costs, and significantly improve the time to insight.
Journal ArticleDOI
Direct numerical simulation of ignition front propagation in a constant volume with temperature inhomogeneities. II. Parametric study
TL;DR: In this paper, the influence of thermal stratification on autoignition at constant volume and high pressure is studied by direct numerical simulation (DNS) with detailed hydrogen/air chemistry.
ReportDOI
Formulas for robust, one-pass parallel computation of covariances and arbitrary-order statistical moments.
TL;DR: A formula for the pairwise update of arbitrary-order centered statistical moments is presented, of particular interest to compute such moments in parallel for large-scale, distributed data sets.
Proceedings ArticleDOI
Analysis of large-scale scalar data using hixels
David Thompson,Joshua A. Levine,Janine C. Bennett,Peer-Timo Bremer,Attila Gyulassy,Valerio Pascucci,Philippe Pierre Pebay +6 more
TL;DR: A new data representation for scalar data, called hixels, that stores a histogram of values for each sample point of a domain is introduced that proposes new feature detection algorithms using a combination of topological and statistical methods.