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7 results for “kinetic theory”
A unifying framework for mean-field theories of asymmetric kinetic Ising systems [Dataset]
<p>Datasets for reproducing the results in the article Aguilera, M., Moosavi, S.A. & Shimazaki, H. A unifying framework for mean-field theories of asymmetric kinetic Ising systems. <em>Nature Communications</em> <strong>12, </strong>1197 (2021). https://doi.org/10.1038/s41467-021-20890-5. Results can be reproduced using the code repository of the article https://github.com/MiguelAguilera/kinetic-Plefka-expansions</p> <p>The main dataset contains simulations of an asymmetric, kinetic Sherrington-Kirkpatrick (SK) model around the equivalent of a ferromagnetic phase transition in the equilibrium SK model. External fields <span class="math-tex">\(H_i\)</span> are sampled from independent uniform distributions <span class="math-tex">\(\mathcal{U}(-\beta H_0, \beta H_0)\)</span> with <span class="math-tex">\(H_0=0.5\)</span>, whereas coupling terms <span class="math-tex">\(J_{ij}\)</span> are sampled from independent Gaussian distributions <span class="math-tex">\(\mathcal{N}(\beta \frac{J_0}{N},\beta^2 \frac{J_\sigma^2}{N})\)</span>, with <span class="math-tex">\(J_0=1, J_\sigma = 0.1\)</span> where <span class="math-tex">\(\beta\)</span> is a scaling parameter (i.e., an inverse temperature).</p> <p>To study the non-stationary transient dynamics of the model, we start from <span class="math-tex">\(\mathbf s_0 = \mathbf 1\)</span> (all elements set to 1 at <span class="math-tex">\(t=0\)</span>) and recursively update its state for <span class="math-tex">\(T=128\)</span> steps. We repeated this stochastic simulation for <span class="math-tex">\(10^6\)</span> trials for 21 values of <span class="math-tex">\(\beta\)</span> in the range <span class="math-tex">\([0.7\beta_c, 1.3\beta_c]\)</span>, except for the reconstruction of the phase transition where we used <span class="math-tex">\(R=10^5\)</span> and 201 values of <span class="math-tex">\(\beta\)</span> in the same range.<br> <br> Each file is stored in: 'data-H0-0.5-J0-1.0-Js-0.1-N-512-R-1000000-beta-[beta_ref].npz', where [beta_ref] contains the normalized value of <span class="math-tex">\(\beta/\beta_C\)</span> between 0.7 and 1.3.<br> <br> Furthermore, data in the folders 'forward.zip', 'inverse.zip' and 'reconstruction.zip' contain files to reproduce the results of the paper above. These files show the results of solving the forward Ising problem, the inverse Ising problem, and the reconstruction of the phase transition combining forward and inverse problems.</p>
Data files for the manuscript "Extended kinetic theory applied to pressure-controlled shear flows of frictionless spheres between rigid, bumpy planes"
<p>This depository contains the data of all DEM simulations used in the manuscript titled "Extended kinetic theory applied to pressure-controlled shear flows of frictionless spheres between rigid, bumpy planes" submitted to Soft Matter in July 2024.</p> <p>The data in the excel file are the measurements obtained after the coarse graining procedure.</p>
Discrete and continuum simulations of bedload transport with kinetic theory of granular flow
<p>This depository contains the simulation results used in the publication Chassagne, R., Chauchat J. & Cyrille B. (2023). A frictional-collisional model for bedload transport based on kinetic theory of granular flows: discrete and continuum approaches.</p> <p>It contains DEM results as well as the results of the continuum model. The results are written in text files containing headers with name and units of the variables.</p>
Data for Theory, Observations, and Simulations of Kinetic Entropy in a Magnetotail Electron Diffusion Region
<p>This .rar file contains the simulation data used in the paper titled "Theory, Observations, and Simulations of Kinetic Entropy in a Magnetotail ElectronDiffusion Region" along with needed instructions for reproducing the simulation plots shown in the paper. </p>
Kinetics of N2 Release from Diazo Compounds: A Combined Machine Learning-Density Functional Theory Study
<p>Total potential (E) and Thermal correction to Gibbs Free Energies obtained using SMD/M06-2X/def2-TZVP//SMD/M06-2X/6-31G(d) level of theory in dichloroethane and Cartesian coordinates for all of the calculated structures.</p> <p>dataset</p> <p>Python Machine Learning Script</p> <p> </p>
Kinetic theory of drift-mirror mode
Open the record for dataset details and reuse information.
Data for Kinetic Entropy-Based Measures of Distribution Function Non-Maxwellianity: Theory and Simulations
<p>*************************************************<br> Data for Fig. 1:<br> *************************************************<br> nonmax_bimax_kp_1.csv is a CSV file with the data for the black curve in Figure 1. There are 1000 lines, and each line contains two numbers - the analytic value of Kaufmann and Paterson non-Maxwellianity for a bi-Maxwellian distribution and T_perp / T_parallel.</p> <p>nonmax_bimax_kp_2.csv is a CSV file with the data for the red curve in Figure 1. There are 1099 lines, and each line contains two numbers - the analytic value of Kaufmann and Paterson non-Maxwellianity for a bi-Maxwellian distribution and T_perp / T_parallel.</p> <p>*************************************************<br> Data for Fig. 2:<br> *************************************************<br> Below are the general instructions which might be useful for reproducing Fig 2 plots<br> 1. read in the .csv files and associate the variable according to the file names<br> 2. To plot Fig 2 (a), do a 2D plot of (b,Phi) vs s_Eg. b corresponds the column of s_Eg and Phi corresponds to the row of s_Eg.<br> Similarly, to plot Fig 2 (d), do a 2D plot of (b,Phi) vs Mbar_KP. Again, b corresponds the column of Mbar_KP and Phi corresponds to the row of Mbar_KP.<br> 3. Fig 2 (b) and (c) are cuts of (a) at particular values of Phi and b respectively.<br> Similarly,Fig 2 (e) and (f) are cuts of (d) at particular values of Phi and b respectively.</p> <p>*************************************************<br> Data for Fig. 3:<br> *************************************************<br> Below are the general instructions which might be useful for reproducing Fig 3 plots<br> 1. read in the .csv files and associate the variable according to the file names. Here, n_norm = n/n_infinity.<br> 2. To plot Fig 3 (a), do a 2D plot of (b,n_norm) vs s_Eg. b corresponds the column of s_Eg and n_norm corresponds to the row of s_Eg.<br> Similarly, to plot Fig 3 (d), do a 2D plot of (b,n_norm) vs Mbar_KP. Again, b corresponds the column of Mbar_KP and n_norm corresponds to the row of Mbar_KP.<br> 3. Fig 3 (b) and (c) are cuts of (a) at particular values of n_norm and b respectively.<br> Similarly, Fig 3 (e) and (f) are cuts of (d) at particular values of n_norm and b respectively.</p> <p>*************************************************<br> Data for Fig. 4:<br> *************************************************<br> nonmax_bimax_1.csv is a CSV file with the data for the black curve in Figure 4. There are 1000 lines, and each line contains two numbers - the analytic value of the non-Maxwellianity for a bi-Maxwellian distribution and T_perp / T_parallel.</p> <p>nonmax_bimax_2.csv is a CSV file with the data for the red curve in Figure 4. There are 399997 lines, and each line contains two numbers - the analytic value of non-Maxwellianity for a bi-Maxwellian distribution and the base 10 logarithm of T_perp / T_parallel.</p> <p>*************************************************<br> Mbar for Figs. 5-9:<br> *************************************************<br> The Mbar_data.csv is a CSV file saving magnetic field Bx, By, the Kaufmann and Paterson non-Maxwellianity (Mbare_KP for electrons, Mbari_KP for ions) and the new defination of non-Maxwellianity (Mbare_norm for electrons, Mbari_norm for ions) as a function of location (x,y) in the simulation domain.</p> <p>The x consists of 4096 data points from 0.00625 to 51.19375.<br> The y consists of 2048 data points from 0.00625 to 25.59375.</p> <p>The x-point location is (x0,y0)=(11.15625,19.24375). The plots in Figures 5-6,7a,8a, and 9a are used relative coordinates (x-x0,y-y0). The magnetic field data can be used to plot the field lines as contours of vector potential Az(x,y) as a function of Bx(x,y) and By(x,y) in Figures 5-6. Figures 7a, 8a, 9a are cuts at x-x0=0,8,13 respectively.</p> <p>*************************************************<br> Distribution Functions for Figs 7-9:<br> *************************************************<br> The CSV files are named as "DF_X#_Y#.csv". The two "#" are the (x-x0,y-y0) coordinates of the distribution function. In each of the files, the data includes three columns. The first two columns are either the vx and vy for Figure 7b or v_par and v_perp1 for Figures 8b and 9b. The last column is the corresponding values of the distribution functions.</p>
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