typoss

Signed-off-by: Umberto Zerbinati <[email protected]>

docs/src/PETScPC/oseen.py.rst

@@ -3,8 +3,8 @@ Vertex Patch smoothing for Augmented Lagrangian formulations of the Oseen proble
 
 In this tutorial, we will see how to use an augmented Lagrangian formulation to precondition the Oseen problem, i.e.
 
-.. math::
-   
+.. math::       
+
    \text{Given }\vec{\beta}\in \mathbb{R}^3 \text{ find } (\vec{u},p) \in [H^1_{0}(\Omega)]^d\times L^2(\Omega) \text{ s.t. }
    
    \begin{cases} 

docs/src/PETScPC/stokes.py.rst

@@ -10,7 +10,7 @@ In particular, we will consider a Bernardi-Raugel inf-sup stable discretization
    
    \begin{cases} 
       (\nabla \vec{u},\nabla \vec{v})_{L^2(\Omega)} + (\nabla\cdot \vec{v}, p)_{L^2(\Omega)}  = (\vec{f},\vec{v})_{L^2(\Omega)} \qquad \forall v\in H^1_{0}(\Omega)\\
-      (\nabla\cdot \vec{u},q)_{L^2(\Omega)} = 0 \qquad \froall q\in L^2(\Omega)
+      (\nabla\cdot \vec{u},q)_{L^2(\Omega)} = 0 \qquad \forall q\in L^2(\Omega)
    \end{cases}
 
 Such a discretization can easily be constructed using NGSolve as follows: ::

docs/src/PETScSNES/hyperelasticity.py.rst

@@ -89,4 +89,3 @@ We compare the performance of the two solvers, in the following table:
      - 10
 
 This suggests that while NGS non-linear solver when finely tuned performs as well as PETSc SNES, it is more sensitive to the choice of the damping factor. In this case, a damping factor of 0.3 was found to be the best choice.
-"""