Updated README

README.md

@@ -1,12 +1,12 @@
-# jaxLDL: JAX implementation of Lagrangian Deep Learning
+# JERALD: high-fidelity dark matter, stellar mass and neutral hydrogen maps from fast N-body simulations
 
 * [**Installation**](#installation)
 * [**Testing**](#testing)
 * [**Documentation**](#documentation)
 
-jaxLDL is the [JAX](https://github.com/google/jax) implementation of the [code](https://github.com/biweidai/LDL) performing [Lagrangian Deep Learning](https://arxiv.org/abs/2010.02926), a machine learning method to paint baryons and related quantities on top of N-body only simulations.
+JERALD is a code, based on the [Lagrangian Deep Learning](https://arxiv.org/abs/2010.02926) idea of Dai and Seljak, to paint baryons and related quantities on top of N-body only simulations with machine learning.
 
-The package is based on JAX and [mpi4jax](https://github.com/mpi4jax/mpi4jax/tree/master), and it uses MPI to implement parallel Cloud-In-Cell (CIC) painting and interpolation algorithms as well as parallel forward and backward Fourier transforms via [FFTW](https://www.fftw.org/).
+The package is based on [JAX](https://github.com/google/jax) and [mpi4jax](https://github.com/mpi4jax/mpi4jax/tree/master), and it uses MPI to implement parallel Cloud-In-Cell (CIC) painting and interpolation algorithms as well as parallel forward and backward Fourier transforms via [FFTW](https://www.fftw.org/).
 
 ## Installation
 To install the package, clone the repo and run:
@@ -20,7 +20,7 @@ CUDA support isn't required, as for the time being the package only runs on CPU.
 On a cluster, I suggest loading any MPI modules and then installing mpi4jax via pip, to allow mpi4py to build on the existing MPI installation.
 
 ## Testing
-A simple test that computes the LDL loss and its derivative with respect to the LDL parameters (checked against the original code) is available in ```losstest.py```. To run it with a single MPI process, execute
+A simple test that computes the model loss and its derivative with respect to its parameters (checked against the original LDL code) is available in ```losstest.py```. To run it with a single MPI process, execute
 ```
 python losstest.py
 ```