Center for Astrophysics, Space Physics/Engineering Research

Data repository for the Center for Astrophysics, Space Physics/Engineering Research at Baylor University

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1 to 10 of 44 Results

Matlab files Feb 18, 2022 - Dust and Ion Dynamics Matthews, Lorin, 2020, "Matlab files", https://doi.org/10.18738/T8/KZLM7L, Texas Data Repository, V3 Matlab scripts used to produce the graphs in "Dust Charging in Dynamic Ion Wakes". An overview of each script and the data files it requires is given in "supporting_data_and_code.docx"
Replication Data for: The initial structure of chondrule dust rims II: charged grains Feb 1, 2022 - The initial structure of chondrule dust rims II: charged grains Xiang, Chuchu, 2022, "Replication Data for: The initial structure of chondrule dust rims II: charged grains", https://doi.org/10.18738/T8/J4ZQJC, Texas Data Repository, V1 Matlab code to analyze results and reproduce figures
The initial structure of chondrule dust rims II: charged grains(Baylor University) Feb 1, 2022Chondrule Formation
The initial structure of chondrule dust rims I: electrically neutral grains Feb 1, 2022 - Chondrule Formation Matthews, Lorin, 2022, "The initial structure of chondrule dust rims I: electrically neutral grains", https://doi.org/10.18738/T8/4TPCQC, Texas Data Repository, V1 Publication on chondrule rims
Chondrule Formation(Baylor University) Feb 1, 2022 Programs and data associated with publications on formation of chondrule rims
Replication Data for: Dust as Probes Apr 29, 2021 - Dust as Probes determining confinement and interaction forces Ashrafi, Khandaker Sharmin, 2021, "Replication Data for: Dust as Probes", https://doi.org/10.18738/T8/3ZJGI1, Texas Data Repository, V1 Final data set and Matlab files used to produce figures in publication
Dust as Probes determining confinement and interaction forces(Baylor University) Apr 29, 2021 Replication data for" Dust as probes: determining confinement and interaction forces", Ashrafi et al., Physical Review E, 102, 043210, 2020
Replication Data for: A machine learning-based Bayesian optimization solution to nonlinear responses in dusty plasmas Mar 25, 2021 - A machine learning-based Bayesian optimization solution to nonlinear responses in dusty plasmas Matthews, Lorin; Ding, Zhiyue; Hyde, Truell, 2021, "Replication Data for: A machine learning-based Bayesian optimization solution to nonlinear responses in dusty plasmas", https://doi.org/10.18738/T8/2YVAGU, Texas Data Repository, V1 Python code for machine learning method to extract coefficients for equation of motion of two interacting dust particles in a plasma environment. Data from experimental measurement of the thermal motion of two vertically-aligned particles are given in the Matlab workspace.
A machine learning-based Bayesian optimization solution to nonlinear responses in dusty plasmas(Baylor University) Mar 25, 2021 Replication files for: A machine learning-based Bayesian optimization solution to nonlinear responses in dusty plasmas, Z. Ding, L. S. Matthews and T. W. Hyde, Machine Learning Science and Technology, accepted February 2021. https://iopscience.iop.org/article/10.1088/2632-2153/ab...
Aggregate_builder_constant_population Mar 22, 2021 - Aggregate Builder Constant Population Matthews, Lorin, 2021, "Aggregate_builder_constant_population", https://doi.org/10.18738/T8/CPP7OC, Texas Data Repository, V1 Aggregate Builder constant population -- uses a MC algorithm to select colliding pair based on the collision probability of all possible pairs. Chooses an elapsed time interval for each collision.

Matlab files

Feb 18, 2022 - Dust and Ion Dynamics

Matthews, Lorin, 2020, "Matlab files", https://doi.org/10.18738/T8/KZLM7L, Texas Data Repository, V3

Matlab scripts used to produce the graphs in "Dust Charging in Dynamic Ion Wakes". An overview of each script and the data files it requires is given in "supporting_data_and_code.docx"

Replication Data for: The initial structure of chondrule dust rims II: charged grains

Feb 1, 2022 - The initial structure of chondrule dust rims II: charged grains

Xiang, Chuchu, 2022, "Replication Data for: The initial structure of chondrule dust rims II: charged grains", https://doi.org/10.18738/T8/J4ZQJC, Texas Data Repository, V1

Matlab code to analyze results and reproduce figures

The initial structure of chondrule dust rims II: charged grains(Baylor University)

Feb 1, 2022Chondrule Formation

The initial structure of chondrule dust rims I: electrically neutral grains

Feb 1, 2022 - Chondrule Formation

Matthews, Lorin, 2022, "The initial structure of chondrule dust rims I: electrically neutral grains", https://doi.org/10.18738/T8/4TPCQC, Texas Data Repository, V1

Publication on chondrule rims

Chondrule Formation(Baylor University)

Feb 1, 2022

Programs and data associated with publications on formation of chondrule rims

Replication Data for: Dust as Probes

Apr 29, 2021 - Dust as Probes determining confinement and interaction forces

Ashrafi, Khandaker Sharmin, 2021, "Replication Data for: Dust as Probes", https://doi.org/10.18738/T8/3ZJGI1, Texas Data Repository, V1

Final data set and Matlab files used to produce figures in publication

Dust as Probes determining confinement and interaction forces(Baylor University)

Apr 29, 2021

Replication data for" Dust as probes: determining confinement and interaction forces", Ashrafi et al., Physical Review E, 102, 043210, 2020

Replication Data for: A machine learning-based Bayesian optimization solution to nonlinear responses in dusty plasmas

Mar 25, 2021 - A machine learning-based Bayesian optimization solution to nonlinear responses in dusty plasmas

Matthews, Lorin; Ding, Zhiyue; Hyde, Truell, 2021, "Replication Data for: A machine learning-based Bayesian optimization solution to nonlinear responses in dusty plasmas", https://doi.org/10.18738/T8/2YVAGU, Texas Data Repository, V1

Python code for machine learning method to extract coefficients for equation of motion of two interacting dust particles in a plasma environment. Data from experimental measurement of the thermal motion of two vertically-aligned particles are given in the Matlab workspace.

A machine learning-based Bayesian optimization solution to nonlinear responses in dusty plasmas(Baylor University)

Mar 25, 2021

Replication files for: A machine learning-based Bayesian optimization solution to nonlinear responses in dusty plasmas, Z. Ding, L. S. Matthews and T. W. Hyde, Machine Learning Science and Technology, accepted February 2021. https://iopscience.iop.org/article/10.1088/2632-2153/ab...

Aggregate_builder_constant_population

Mar 22, 2021 - Aggregate Builder Constant Population

Matthews, Lorin, 2021, "Aggregate_builder_constant_population", https://doi.org/10.18738/T8/CPP7OC, Texas Data Repository, V1

Aggregate Builder constant population -- uses a MC algorithm to select colliding pair based on the collision probability of all possible pairs. Chooses an elapsed time interval for each collision.

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