Navier-Stokes in readme

README.md

@@ -6,7 +6,8 @@
   <tr>
     <th><img src="docs/assets/laplace.png" width="180"></th>
     <th><img src="docs/assets/adv_diff_periodic.gif" width="350"></th>
-    <th><img src="docs/assets/burgers_u.gif" width="188"></th>
+    <!-- <th><img src="docs/assets/burgers_u.gif" width="188"></th> -->
+    <th><img src="docs/assets/ns_norm.gif" width="188"></th>
   </tr>
 </table>
 
@@ -19,7 +20,7 @@
 - Support for Dirichlet, Neumann, Robin, and Periodic boundary conditions
 - Automatic generation of normals from 2D GMSH meshes
 
-𝕌pdes in incredibly extendable, with additional features added frequently.
+𝕌pdes is easily extendable, with additional features added frequently.
 
 
 ## Getting started
@@ -62,28 +63,28 @@ sol = updes.pde_solver_jit(diff_operator=my_diff_operator,
 cloud.visualize_field(sol.vals, cmap="jet", projection="3d", title="RBF solution")
 ```
 
-𝕌pdes can handle much complicated cases with little to no modifications to the code above. Check out further notebooks and scripts in the documentation and the folder [`demos`](./demos)!
+𝕌pdes can handle much complicated cases with little modifications to the code above. Check out the [documentation](https://ddrous.github.io/Updes/) and Python interactive notebooks in the [`demos`](./demos) folder !
 
 
 
 
 ## To-Dos
-- Logo, contributors guide, and developer documentation
-- Better introductory examples and user documentation for outreach:
-    - Integration with Neural Networks and Equinox
+1. Logo, contributors guide, and developer documentation
+2. More introductory examples in the documentation :
+    - Integration with neural networks and [Equinox](https://github.com/patrick-kidger/equinox)
     - Non-linear and multi-dimensional PDEs
     - Adjoint schemes for fluid flows
-- Better point generation with accurate geometry and normals: 
+3. Better point generation with accurate geometry and normals: 
     - USD format
-    - Gmsh tutorial
-- Add support for 3D radial basis functions
+    - GMSH tutorial
+4. Support for 3D radial basis functions
 
 We welcome contributions from the community. Please feel free to open an issue or a pull request.
 
 
 ## Dependencies
-- **Core**: JAX - GMSH - Matplotlib - Seaborn - Scikit-Learn
-- **Optional**: PyVista - FFMPEG - QuartoDoc
+- **Core**: [JAX](https://github.com/google/jax) - [GMSH](https://pypi.org/project/gmsh/) - [Matplotlib](https://github.com/matplotlib/matplotlib) - [Seaborn](https://github.com/mwaskom/seaborn) - [Scikit-Learn](https://github.com/scikit-learn/scikit-learn)
+- **Optional**: [PyVista](https://github.com/pyvista/pyvista) - [FFMPEG](https://github.com/kkroening/ffmpeg-python) - [QuartoDoc](https://github.com/machow/quartodoc/)
 
 See the `pyproject.toml` file the specific versions of the dependencies.
 

demos/NavierStokes/001_unit_square_rotation.py

@@ -38,8 +38,8 @@
 
 ## Diffusive constant
 
-Nx = 15
-Ny = 15
+Nx = 20
+Ny = 20
 SUPPORT_SIZE = "max"
 
 facet_types_vel={"South":"d", "North":"d", "West":"d", "East":"d"}
@@ -214,7 +214,7 @@ def Phi_x_Phi_y(x, y):
 
 normlist = [jnp.sqrt(sol[0]**2 + sol[1]**2) for sol in sol_list]
 filename = DATAFOLDER + "ns_norm.gif"
-cloud_vel.animate_fields([normlist], cmaps="coolwarm", filename=filename, levels=80, duration=10, figsize=(7.5,6), titles=["Navier Stokes with RBFs - norm"]);
+cloud_vel.animate_fields([normlist[::10]], cmaps="coolwarm", filename=filename, levels=200, duration=10, figsize=(7.5,6), titles=["Navier-Stokes with RBFs"]);
 
 
 # %%