Scalable Approaches for Uncertainty Quantification in High Performance Computing

Dec 11, 2025

180 likes | 284 Vues

This workshop focuses on advanced methods for uncertainty quantification in high-performance computing environments. Rick Archibald from the Oak Ridge National Laboratory presents scalable approximation techniques and error estimation specifically tailored for stochastic collocation. Attendees will explore approaches employing Monte Carlo simulations, sparse grids, and polynomial and wavelet basis functions. Insights into the latest research developments and practical applications in uncertainty quantification will be shared, aiming to enhance computational efficiency and accuracy in modeling complex systems.

imogene-weber

Télécharger la présentation

Scalable Approaches for Uncertainty Quantification in High Performance Computing

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

Uncertainty Quantification Methods for High Performance Computing: Scalable Approximation and Error Estimation for Stochastic Collocation Rick Archibald Oak Ridge National Laboratory SAMSI Transition Workshop Research Triangle Park, NC May 2012