Hard Rod Fluid Analytical Solution in .npy (For Operator learning)

<p>Hard rod fluid with diameter of 1.0 between 2 walls with distance L+2d=10.0,<br>under different chemical potential (mu) and external potential (V_ext(x)), with a sampling distance dz=0.1.</p> <p>Data are stored as numpy structured ndarrays, each npy file store one type of data.&nbsp;<br>The ndarrays has the shape of (n_sample,n_grid), with 4 fields:<br>the position "z",&nbsp;<br>the density profile "rho",&nbsp;<br>the local chemical potential "muloc"<br>the one-body direct correlation function "c1".<br>Where muloc(z)=mu-V_ext(z)</p> <p>In Operator Learning, I've tried the mapping of rho to c1 as well as muloc to rho.</p> <p>dataload.py contains some functions used for loading data<br>data_model_train.py contains the read-in function for model-training data.</p> <p>data_4groups.npy saves 8000 data, with 2000 in each group, generated from the following external potentials:<br>group 1: V_ext=0&nbsp;<br>group 2: V_binary[z] = -epsilon*((a/(z+a/2))^3+(a/(L+a/2-z))^3)<br>group 3: V_binary[z] = -mg*z<br>group 4: V_binary[z] = -u0*(abs(z/z0))^a0<br>with mu, epsilon, a, mg, u0, z0, a0 are adjustable constants of the potential&nbsp;</p> <p>data_mixed23.npy saves 2000 data, generated from the following external potentials:<br>&nbsp; &nbsp; V_binary[z] = -epsilon*((a/(z+a/2))^3+(a/(L+a/2-z))^3) -mg*z</p> <p>Folders are taped in .zip files.</p> <p>Folder "~/R_data_generator" stores the generator of the data, analytical solution of 1D hard rod fluid.<br>Folder "~/Rdata" contains same data in .Rdata file, as R matrixs<br>Folder "~/R2py" contains the script to generate .npy file from .Rdata file</p>