Distributed database kriging for adaptive sampling (D²KAS)

Roehm, Dominic; Pavel, Robert S.; Barros, Kipton; Rouet-Leduc, Bertrand; McPherson, Allen L.; Germann, Timothy C.; Junghans, Christoph

doi:10.1016/j.cpc.2015.03.006

Title: Distributed database kriging for adaptive sampling (D²KAS)

Journal Article · Wed Mar 18 00:00:00 EDT 2015 · Computer Physics Communications

DOI:https://doi.org/10.1016/j.cpc.2015.03.006· OSTI ID:1193447

Roehm, Dominic ^[1]; Pavel, Robert S. ^[2]; Barros, Kipton ^[3]; Rouet-Leduc, Bertrand ^[4]; McPherson, Allen L. ^[3]; Germann, Timothy C. ^[3]; Junghans, Christoph ^[3]

Universitat Stuttgart, Stuttgart (Germany); Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Univ. of Delaware, Newark, DE (United States); Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Univ. of Cambridge, Cambridge (United Kingdom); Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

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.

View Accepted Manuscript (DOE)

View Accepted Manuscript (Publisher)

Cite

Export

Save

Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC52-06NA25396

OSTI ID:: 1193447

Alternate ID(s):: OSTI ID: 1246978

Report Number(s):: LA-UR-14-27213; PII: S0010465515001058; TRN: US1600410

Journal Information:: Computer Physics Communications, Vol. 192, Issue C; ISSN 0010-4655

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

Citation Metrics:

Cited by: 19 works

Citation information provided by
Web of Science

References (24)

Extracting macroscopic dynamics: model problems and algorithms Givon, Dror; Kupferman, Raz; Stuart, Andrew Nonlinearity, Vol. 17, Issue 6 https://doi.org/10.1088/0951-7715/17/6/R01	journal	August 2004
Adaptive Multiscale Molecular Dynamics of Macromolecular Fluids Nielsen, Steven O.; Moore, Preston B.; Ensing, Bernd Physical Review Letters, Vol. 105, Issue 23 https://doi.org/10.1103/PhysRevLett.105.237802	journal	December 2010
Assessment of atomistic coarse-graining methods Chen, Youping; Zimmerman, Jonathan; Krivtsov, Anton International Journal of Engineering Science, Vol. 49, Issue 12 https://doi.org/10.1016/j.ijengsci.2011.03.018	journal	December 2011
Hybrid Approaches to Coarse-Graining using the VOTCA Package: Liquid Hexane Rühle, Victor; Junghans, Christoph Macromolecular Theory and Simulations, Vol. 20, Issue 7 https://doi.org/10.1002/mats.201100011	journal	May 2011
Learning with genetic algorithms: An overview De Jong, Kenneth Machine Learning, Vol. 3, Issue 2-3 https://doi.org/10.1007/BF00113894	journal	October 1988
Heterogeneous multiscale method: A general methodology for multiscale modeling E., Weinan; Engquist, Bjorn; Huang, Zhongyi Physical Review B, Vol. 67, Issue 9 https://doi.org/10.1103/PhysRevB.67.092101	journal	March 2003
Multiscale modeling of the dynamics of solids at finite temperature Li, Xiantao; E., Weinan Journal of the Mechanics and Physics of Solids, Vol. 53, Issue 7 https://doi.org/10.1016/j.jmps.2005.01.008	journal	July 2005
Equation-Free, Coarse-Grained Multiscale Computation: Enabling Mocroscopic Simulators to Perform System-Level Analysis Gear, C. William; Hyman, James M.; Kevrekidid, Panagiotis G. Communications in Mathematical Sciences, Vol. 1, Issue 4 https://doi.org/10.4310/CMS.2003.v1.n4.a5	journal	January 2003
Nonoscillatory Central Schemes for Multidimensional Hyperbolic Conservation Laws Jiang, Guang-Shan; Tadmor, Eitan SIAM Journal on Scientific Computing, Vol. 19, Issue 6 https://doi.org/10.1137/S106482759631041X	journal	November 1998
High-Resolution Nonoscillatory Central Schemes with Nonstaggered Grids for Hyperbolic Conservation Laws Jiang, G. S.; Levy, D.; Lin, C. T. SIAM Journal on Numerical Analysis, Vol. 35, Issue 6 https://doi.org/10.1137/S0036142997317560	journal	December 1998
New High-Resolution Central Schemes for Nonlinear Conservation Laws and Convection–Diffusion Equations Kurganov, Alexander; Tadmor, Eitan Journal of Computational Physics, Vol. 160, Issue 1 https://doi.org/10.1006/jcph.2000.6459	journal	May 2000
Adaptive sampling in hierarchical simulation Knap, J.; Barton, N. R.; Hornung, R. D. International Journal for Numerical Methods in Engineering, Vol. 76, Issue 4 https://doi.org/10.1002/nme.2339	journal	October 2008
Embedded polycrystal plasticity and adaptive sampling Barton, Nathan R.; Knap, Jaroslaw; Arsenlis, Athanasios International Journal of Plasticity, Vol. 24, Issue 2 https://doi.org/10.1016/j.ijplas.2007.03.004	journal	February 2008
Spatial adaptive sampling in multiscale simulation Rouet-Leduc, Bertrand; Barros, Kipton; Cieren, Emmanuel Computer Physics Communications, Vol. 185, Issue 7 https://doi.org/10.1016/j.cpc.2014.03.011	journal	July 2014
Environmental data mining and modeling based on machine learning algorithms and geostatistics Kanevski, M.; Parkin, R.; Pozdnukhov, A. Environmental Modelling & Software, Vol. 19, Issue 9 https://doi.org/10.1016/j.envsoft.2003.03.004	journal	September 2004
Intramolecular polarisable multipolar electrostatics from the machine learning method Kriging Mills, Matthew J. L.; Popelier, Paul L. A. Computational and Theoretical Chemistry, Vol. 975, Issue 1-3 https://doi.org/10.1016/j.comptc.2011.04.004	journal	November 2011
Optimal construction of a fast and accurate polarisable water potential based on multipole moments trained by machine learning Handley, Chris M.; Hawe, Glenn I.; Kell, Douglas B. Physical Chemistry Chemical Physics, Vol. 11, Issue 30 https://doi.org/10.1039/b905748j	journal	January 2009
The Statistical Mechanical Theory of Transport Processes. IV. The Equations of Hydrodynamics Irving, J. H.; Kirkwood, John G. The Journal of Chemical Physics, Vol. 18, Issue 6 https://doi.org/10.1063/1.1747782	journal	June 1950
High Resolution Schemes Using Flux Limiters for Hyperbolic Conservation Laws Sweby, P. K. SIAM Journal on Numerical Analysis, Vol. 21, Issue 5 https://doi.org/10.1137/0721062	journal	October 1984
Spatial prediction and ordinary kriging Cressie, Noel Mathematical Geology, Vol. 20, Issue 4 https://doi.org/10.1007/BF00892986	journal	May 1988
The origins of kriging Cressie, Noel Mathematical Geology, Vol. 22, Issue 3 https://doi.org/10.1007/BF00889887	journal	April 1990
SQL databases v. NoSQL databases Stonebraker, Michael Communications of the ACM, Vol. 53, Issue 4 https://doi.org/10.1145/1721654.1721659	journal	April 2010
Scalable SQL and NoSQL data stores Cattell, Rick ACM SIGMOD Record, Vol. 39, Issue 4 https://doi.org/10.1145/1978915.1978919	journal	May 2011
Structural stability and lattice defects in copper: Ab initio , tight-binding, and embedded-atom calculations Mishin, Y.; Mehl, M. J.; Papaconstantopoulos, D. A. Physical Review B, Vol. 63, Issue 22 https://doi.org/10.1103/PhysRevB.63.224106	journal	May 2001

Cited By (7)

Hierarchical multiscale framework for materials modeling: Equation of state implementation and application to a Taylor anvil impact test of RDX Barnes, Brian C.; Spear, Carrie E.; Leiter, Ken W. SHOCK COMPRESSION OF CONDENSED MATTER - 2015: Proceedings of the Conference of the American Physical Society Topical Group on Shock Compression of Condensed Matter, AIP Conference Proceedings https://doi.org/10.1063/1.4971607	conference	January 2017
Database assisted distribution to improve fault tolerance for multiphysics applications Pavel, Robert S.; McPherson, Allen L.; Germann, Timothy C. Proceedings of the 2nd International Workshop on Hardware-Software Co-Design for High Performance Computing - Co-HPC '15 https://doi.org/10.1145/2834899.2834908	conference	January 2015
Toward a Predictive Hierarchical Multiscale Modeling Approach for Energetic Materials Barnes, Brian C.; Brennan, John K.; Byrd, Edward F. C. Computational Approaches for Chemistry Under Extreme Conditions https://doi.org/10.1007/978-3-030-05600-1_10	book	February 2019
The heterogeneous multiscale method applied to inelastic polymer mechanics Vassaux, M.; Richardson, R. A.; Coveney, P. V. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 377, Issue 2142 https://doi.org/10.1098/rsta.2018.0150	journal	February 2019
Toward High Fidelity Materials Property Prediction from Multiscale Modeling and Simulation Vassaux, Maxime; Sinclair, Robert C.; Richardson, Robin A. Advanced Theory and Simulations, Vol. 3, Issue 1 https://doi.org/10.1002/adts.201900122	journal	October 2019
Mastering the scales: a survey on the benefits of multiscale computing software Groen, Derek; Knap, Jaroslaw; Neumann, Philipp Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 377, Issue 2142 https://doi.org/10.1098/rsta.2018.0147	journal	February 2019
Efficient Streaming Mass Spatio-Temporal Vehicle Data Access in Urban Sensor Networks Based on Apache Storm Zhou, Lianjie; Chen, Nengcheng; Chen, Zeqiang Sensors, Vol. 17, Issue 4 https://doi.org/10.3390/s17040815	journal	April 2017

Similar Records

Machine-learning-based dynamic-importance sampling for adaptive multiscale simulations

Journal Article · Thu Apr 22 00:00:00 EDT 2021 · Nature Machine Intelligence · OSTI ID:1193447

Bhatia, Harsh; Carpenter, Timothy S.; Ingólfsson, Helgi I.; +9 more

An adaptive Kriging surrogate method for efficient uncertainty quantification with an application to geological carbon sequestration modeling

Journal Article · Tue Jan 29 00:00:00 EST 2019 · Computers and Geosciences · OSTI ID:1193447

Mo, Shaoxing; Shi, Xiaoqing; Lu, Dan; +2 more

Evaluation of kriging based surrogate models constructed from mesoscale computations of shock interaction with particles

Journal Article · Mon May 01 00:00:00 EDT 2017 · Journal of Computational Physics · OSTI ID:1193447

Sen, Oishik; Jacobs, Gustaaf

Related Subjects

74 ATOMIC AND MOLECULAR PHYSICS
36 MATERIALS SCIENCE
97 MATHEMATICS AND COMPUTING
adaptive sampling
heterogeneous multiscale methods
elastodynamics
cloud database
kriging

Title: Distributed database kriging for adaptive sampling (D²KAS)

Citation Formats

References (24)

Cited By (7)

Similar Records

Related Subjects