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Title: Distributed database kriging for adaptive sampling (D²KAS)

Abstract

We present an adaptive sampling method supplemented by a distributed database and a prediction method for multiscale simulations using the Heterogeneous Multiscale Method. A finite-volume scheme integrates the macro-scale conservation laws for elastodynamics, which are closed by momentum and energy fluxes evaluated at the micro-scale. In the original approach, molecular dynamics (MD) simulations are launched for every macro-scale volume element. Our adaptive sampling scheme replaces a large fraction of costly micro-scale MD simulations with fast table lookup and prediction. The cloud database Redis provides the plain table lookup, and with locality aware hashing we gather input data for our prediction scheme. For the latter we use kriging, which estimates an unknown value and its uncertainty (error) at a specific location in parameter space by using weighted averages of the neighboring points. We find that our adaptive scheme significantly improves simulation performance by a factor of 2.5 to 25, while retaining high accuracy for various choices of the algorithm parameters.

Authors:
 [1];  [2];  [3];  [4];  [3];  [3];  [3]
  1. Universitat Stuttgart, Stuttgart (Germany); Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  2. Univ. of Delaware, Newark, DE (United States); Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  3. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  4. Univ. of Cambridge, Cambridge (United Kingdom); Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1193447
Alternate Identifier(s):
OSTI ID: 1246978
Report Number(s):
LA-UR-14-27213
Journal ID: ISSN 0010-4655; PII: S0010465515001058; TRN: US1600410
Grant/Contract Number:  
AC52-06NA25396
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Computer Physics Communications
Additional Journal Information:
Journal Volume: 192; Journal Issue: C; Journal ID: ISSN 0010-4655
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
74 ATOMIC AND MOLECULAR PHYSICS; 36 MATERIALS SCIENCE; 97 MATHEMATICS AND COMPUTING; adaptive sampling; heterogeneous multiscale methods; elastodynamics; cloud database; kriging

Citation Formats

Roehm, Dominic, Pavel, Robert S., Barros, Kipton, Rouet-Leduc, Bertrand, McPherson, Allen L., Germann, Timothy C., and Junghans, Christoph. Distributed database kriging for adaptive sampling (D²KAS). United States: N. p., 2015. Web. doi:10.1016/j.cpc.2015.03.006.
Roehm, Dominic, Pavel, Robert S., Barros, Kipton, Rouet-Leduc, Bertrand, McPherson, Allen L., Germann, Timothy C., & Junghans, Christoph. Distributed database kriging for adaptive sampling (D²KAS). United States. doi:10.1016/j.cpc.2015.03.006.
Roehm, Dominic, Pavel, Robert S., Barros, Kipton, Rouet-Leduc, Bertrand, McPherson, Allen L., Germann, Timothy C., and Junghans, Christoph. Wed . "Distributed database kriging for adaptive sampling (D²KAS)". United States. doi:10.1016/j.cpc.2015.03.006. https://www.osti.gov/servlets/purl/1193447.
@article{osti_1193447,
title = {Distributed database kriging for adaptive sampling (D²KAS)},
author = {Roehm, Dominic and Pavel, Robert S. and Barros, Kipton and Rouet-Leduc, Bertrand and McPherson, Allen L. and Germann, Timothy C. and Junghans, Christoph},
abstractNote = {We present an adaptive sampling method supplemented by a distributed database and a prediction method for multiscale simulations using the Heterogeneous Multiscale Method. A finite-volume scheme integrates the macro-scale conservation laws for elastodynamics, which are closed by momentum and energy fluxes evaluated at the micro-scale. In the original approach, molecular dynamics (MD) simulations are launched for every macro-scale volume element. Our adaptive sampling scheme replaces a large fraction of costly micro-scale MD simulations with fast table lookup and prediction. The cloud database Redis provides the plain table lookup, and with locality aware hashing we gather input data for our prediction scheme. For the latter we use kriging, which estimates an unknown value and its uncertainty (error) at a specific location in parameter space by using weighted averages of the neighboring points. We find that our adaptive scheme significantly improves simulation performance by a factor of 2.5 to 25, while retaining high accuracy for various choices of the algorithm parameters.},
doi = {10.1016/j.cpc.2015.03.006},
journal = {Computer Physics Communications},
number = C,
volume = 192,
place = {United States},
year = {Wed Mar 18 00:00:00 EDT 2015},
month = {Wed Mar 18 00:00:00 EDT 2015}
}

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