Latest revision as of 14:39, 11 February 2021

Abstract

In order to execute Uncertainty Quantification (UQ) studies, the number of reads placed on the filesystem will increase. This document works through the file I/O for a climatology UQ study. The utilization of the filesystem for the above discussed will be similar for ICF and Stockpile Stewardship applications. The current state of the art for the quantification of uncertainty of a multi-physics simulation code is the utilization of an ensemble of models approach. As an overview of the ensemble of models approach, a set of uncertain input parameters is identified along with an identified set of observational and output parameters. The model is computed n number of times with each ensemble simulation using a unique set of parametric combinations of input parameters thereby creating an ensemble of simulations. Response surface models (also known as statistical emulator models, surrogate models, or meta models) are trained using the ensemble results. The response models are then convolved with observational data to further constrain input parameters and to create uncertainty bounds on the model outputs. Using the Community Climate System Model (CCSM) specifically the atmospheric component of CCSM, the Community Atmospheric Model (CAM), as the model of interest, this document provides a rough modelmore » of the demands on a filesystem that is needed to execute a UQ study on CAM. Each ensemble simulation consists of 12 simulation years and uses 384 processors on the Atlas machine. The LLNL UQ Pipeline is LLNL's standard tool to execute UQ studies. The UQ Pipeline possesses the capability to execute the ensemble simulations on LLNL's diverse set of HPC environments, produce response models, generate uncertainty bounds, and analyze the results. The process executing the LLNL UQ Pipeline is run on a different compute node from the set of concurrent, executing ensemble simulations.« le

Original document

The different versions of the original document can be found in:

• http://dx.doi.org/10.2172/972850

• https://www.osti.gov/servlets/purl/972850,

https://academic.microsoft.com/#/detail/2072728689