Publications

Data for publications of the SC group.

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1 to 10 of 36 Results

spinodal_decomposition.py Jun 7, 2021 - Replication Data for: Load-Balancing for Scalable Simulations with Large Particle Numbers Python Source Code - 3.1 KB - MD5: aabac166d774fcd86a201c147093c86d CodeInput
spinodal-load-balancing.py Jun 7, 2021 - Replication Data for: Load-Balancing for Scalable Simulations with Large Particle Numbers Python Source Code - 3.6 KB - MD5: 59bac744bca22e1ebf74236dd0583e13 Code
simulation_script.tcl Jun 7, 2021 - Replication Data for: Load-Balancing for Scalable Simulations with Large Particle Numbers text/x-tcl - 1.6 KB - MD5: b5dbae17801cc245d6c52a427c6b6383 Code
simulation-result-data-extracted.tab Jun 7, 2021 - Replication Data for: Load-Balancing for Scalable Simulations with Large Particle Numbers Tabular Data - 551 B - 1 Variables, 12 Observations - UNF:6:E2YeVaO+v2VpzS+UkjzWWg== DataOutput
simulate-agglomerate.py Jun 7, 2021 - Replication Data for: Load-Balancing for Scalable Simulations with Large Particle Numbers Python Source Code - 2.6 KB - MD5: 73c90f4d397e7ea42f89815290101407 Code
scaling.tab Jun 7, 2021 - Replication Data for: Load-Balancing for Scalable Simulations with Large Particle Numbers Tabular Data - 342 B - 1 Variables, 10 Observations - UNF:6:0J2k1mxPjtjCMq6q8Vbw6g== DataOutput
run.sh Jun 7, 2021 - Replication Data for: Load-Balancing for Scalable Simulations with Large Particle Numbers Shell Script - 522 B - MD5: 0c6b608ca0b39c969dd01d49abd4ecab Code
README_replication.md Apr 20, 2023 - Replication Data for: A mass-conserving sparse grid combination technique with biorthogonal hierarchical basis functions for kinetic simulations Markdown Text - 2.5 KB - MD5: 012c5875ce81838527c4c4864b085df2 Readme
plot.py Jun 7, 2021 - Replication Data for: Load-Balancing for Scalable Simulations with Large Particle Numbers Python Source Code - 2.0 KB - MD5: 99ed5c1b3b041cc61ce076758f9ccfe7 CodePostprocessing
plot-setup.py Jun 7, 2021 - Replication Data for: Load-Balancing for Scalable Simulations with Large Particle Numbers Python Source Code - 1.0 KB - MD5: 143d1fd6c415a21b032aee26e8092ef1 CodePostprocessing Plots the setup. Input is a txt file of the particle positions (agglomerate-1954.txt).

spinodal_decomposition.py

Jun 7, 2021 - Replication Data for: Load-Balancing for Scalable Simulations with Large Particle Numbers

Python Source Code - 3.1 KB -

CodeInput

spinodal-load-balancing.py

Jun 7, 2021 - Replication Data for: Load-Balancing for Scalable Simulations with Large Particle Numbers

Python Source Code - 3.6 KB -

Code

simulation_script.tcl

Jun 7, 2021 - Replication Data for: Load-Balancing for Scalable Simulations with Large Particle Numbers

text/x-tcl - 1.6 KB -

Code

simulation-result-data-extracted.tab

Jun 7, 2021 - Replication Data for: Load-Balancing for Scalable Simulations with Large Particle Numbers

Tabular Data - 551 B - 1 Variables, 12 Observations -

DataOutput

simulate-agglomerate.py

Jun 7, 2021 - Replication Data for: Load-Balancing for Scalable Simulations with Large Particle Numbers

Python Source Code - 2.6 KB -

Code

scaling.tab

Jun 7, 2021 - Replication Data for: Load-Balancing for Scalable Simulations with Large Particle Numbers

Tabular Data - 342 B - 1 Variables, 10 Observations -

DataOutput

run.sh

Jun 7, 2021 - Replication Data for: Load-Balancing for Scalable Simulations with Large Particle Numbers

Shell Script - 522 B -

Code

README_replication.md

Apr 20, 2023 - Replication Data for: A mass-conserving sparse grid combination technique with biorthogonal hierarchical basis functions for kinetic simulations

Markdown Text - 2.5 KB -

Readme

plot.py

Jun 7, 2021 - Replication Data for: Load-Balancing for Scalable Simulations with Large Particle Numbers

Python Source Code - 2.0 KB -

CodePostprocessing

plot-setup.py

Jun 7, 2021 - Replication Data for: Load-Balancing for Scalable Simulations with Large Particle Numbers

Python Source Code - 1.0 KB -

CodePostprocessing

Plots the setup. Input is a txt file of the particle positions (agglomerate-1954.txt).

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