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1 to 10 of 38 Results

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.
Aggregate Builder Constant Population(Baylor University) Mar 22, 2021Growth of Aggregates in a Protoplanetary Disk Matlab code to run aggregate builder -- keeping a constant number of particles in the population to compute the probability of collision for any two aggregates, select next most probable pair and an elapsed time interval
Replication Data for: Detailed model of the growth of fluffy dust aggregates in a protoplanetary disk: Effects of nebular conditions Mar 22, 2021 - Growth of Aggregates in a Protoplanetary Disk Matthews, Lorin, 2021, "Replication Data for: Detailed model of the growth of fluffy dust aggregates in a protoplanetary disk: Effects of nebular conditions", https://doi.org/10.18738/T8/SPIEGK, Texas Data Repository, V1 Matlab code to analyze results and reproduce figures.
Growth of Aggregates in a Protoplanetary Disk(Baylor University) Mar 22, 2021Growth of fluffy dust aggregates in a protoplanetary disk Codes used for analyzing data and creating figures for Detailed model of the growth of fluffy dust aggregates in a protoplanetary disk: Effects of nebular conditions, Astrophysical Journal, 897(2), 182, July 2020. DOI: 10.3847/1538-4357/ab96c2, arXiv:1911.04589
Replication Data for: dust aggregates data Mar 22, 2021 - Growth of fluffy dust aggregates in a protoplanetary disk Matthews, Lorin, 2021, "Replication Data for: dust aggregates data", https://doi.org/10.18738/T8/ZAK5PH, Texas Data Repository, V1 Sample output from the Aggregate_Builder_Constant_Population simulation. This data can be used to create the figures in the paper.
Growth of fluffy dust aggregates in a protoplanetary disk(Baylor University) Mar 22, 2021 Source and analysis code for data contained in the paper "Detailed model of the growth of fluffy dust aggregates in a protoplanetary disk: Effects of nebular conditions", Astrophysical Journal, 897(2), 182, July 2020. DOI: 10.3847/1538-4357/ab96c2
Aug6_M1_Data5 Feb 19, 2020 - Dust and Ion Dynamics Matthews, Lorin, 2020, "Aug6_M1_Data5", https://doi.org/10.18738/T8/FOK0UD, Texas Data Repository, V1 Data files produced by IonWake code used to produce the graphs in "Dust Charging in Dynamic Ion Wakes"
Aug4_M50_Data2 Feb 19, 2020 - Dust and Ion Dynamics Matthews, Lorin, 2020, "Aug4_M50_Data2", https://doi.org/10.18738/T8/AZKZXE, Texas Data Repository, V1 Data files produced by IonWake code used to produce the graphs in "Dust Charging in Dynamic Ion Wakes"

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.

Aggregate Builder Constant Population(Baylor University)

Mar 22, 2021Growth of Aggregates in a Protoplanetary Disk

Matlab code to run aggregate builder -- keeping a constant number of particles in the population to compute the probability of collision for any two aggregates, select next most probable pair and an elapsed time interval

Replication Data for: Detailed model of the growth of fluffy dust aggregates in a protoplanetary disk: Effects of nebular conditions

Mar 22, 2021 - Growth of Aggregates in a Protoplanetary Disk

Matthews, Lorin, 2021, "Replication Data for: Detailed model of the growth of fluffy dust aggregates in a protoplanetary disk: Effects of nebular conditions", https://doi.org/10.18738/T8/SPIEGK, Texas Data Repository, V1

Matlab code to analyze results and reproduce figures.

Growth of Aggregates in a Protoplanetary Disk(Baylor University)

Mar 22, 2021Growth of fluffy dust aggregates in a protoplanetary disk

Codes used for analyzing data and creating figures for Detailed model of the growth of fluffy dust aggregates in a protoplanetary disk: Effects of nebular conditions, Astrophysical Journal, 897(2), 182, July 2020. DOI: 10.3847/1538-4357/ab96c2, arXiv:1911.04589

Replication Data for: dust aggregates data

Mar 22, 2021 - Growth of fluffy dust aggregates in a protoplanetary disk

Matthews, Lorin, 2021, "Replication Data for: dust aggregates data", https://doi.org/10.18738/T8/ZAK5PH, Texas Data Repository, V1

Sample output from the Aggregate_Builder_Constant_Population simulation. This data can be used to create the figures in the paper.

Growth of fluffy dust aggregates in a protoplanetary disk(Baylor University)

Mar 22, 2021

Source and analysis code for data contained in the paper "Detailed model of the growth of fluffy dust aggregates in a protoplanetary disk: Effects of nebular conditions", Astrophysical Journal, 897(2), 182, July 2020. DOI: 10.3847/1538-4357/ab96c2

Aug6_M1_Data5

Feb 19, 2020 - Dust and Ion Dynamics

Matthews, Lorin, 2020, "Aug6_M1_Data5", https://doi.org/10.18738/T8/FOK0UD, Texas Data Repository, V1

Data files produced by IonWake code used to produce the graphs in "Dust Charging in Dynamic Ion Wakes"

Aug4_M50_Data2

Feb 19, 2020 - Dust and Ion Dynamics

Matthews, Lorin, 2020, "Aug4_M50_Data2", https://doi.org/10.18738/T8/AZKZXE, Texas Data Repository, V1

Data files produced by IonWake code used to produce the graphs in "Dust Charging in Dynamic Ion Wakes"

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