update plotter

docs/source/_rst/tutorials/tutorial3/tutorial.rst

@@ -25,14 +25,13 @@ The problem definition
 
 The problem is written in the following form:
 
-.. math::
-    \begin{equation}
-    \begin{cases}
-    \Delta u(x,y,t) = \frac{\partial^2}{\partial t^2} u(x,y,t) \quad \text{in } D, \\\\
-    u(x, y, t=0) = \sin(\pi x)\sin(\pi y), \\\\
-    u(x, y, t) = 0 \quad \text{on } \Gamma_1 \cup \Gamma_2 \cup \Gamma_3 \cup \Gamma_4,
-    \end{cases}
-    \end{equation}
+:raw-latex:`\begin{equation}
+\begin{cases}
+\Delta u(x,y,t) = \frac{\partial^2}{\partial t^2} u(x,y,t) \quad \text{in } D, \\\\
+u(x, y, t=0) = \sin(\pi x)\sin(\pi y), \\\\
+u(x, y, t) = 0 \quad \text{on } \Gamma_1 \cup \Gamma_2 \cup \Gamma_3 \cup \Gamma_4,
+\end{cases}
+\end{equation}`
 
 where :math:`D` is a square domain :math:`[0,1]^2`, and
 :math:`\Gamma_i`, with :math:`i=1,...,4`, are the boundaries of the
@@ -149,7 +148,7 @@ approximately 3 minutes.
 
 .. parsed-literal::
 
-    Epoch 999: : 1it [00:00, 62.13it/s, v_num=0, mean_loss=0.0268, D_loss=0.0397, t0_loss=0.121, gamma1_loss=0.000, gamma2_loss=0.000, gamma3_loss=0.000, gamma4_loss=0.000] 
+    Epoch 999: : 1it [00:00, 84.47it/s, v_num=0, gamma1_loss=0.000, gamma2_loss=0.000, gamma3_loss=0.000, gamma4_loss=0.000, t0_loss=0.0419, D_loss=0.0307, mean_loss=0.0121]
 
 .. parsed-literal::
 
@@ -158,7 +157,7 @@ approximately 3 minutes.
 
 .. parsed-literal::
 
-    Epoch 999: : 1it [00:00, 53.88it/s, v_num=0, mean_loss=0.0268, D_loss=0.0397, t0_loss=0.121, gamma1_loss=0.000, gamma2_loss=0.000, gamma3_loss=0.000, gamma4_loss=0.000]
+    Epoch 999: : 1it [00:00, 68.69it/s, v_num=0, gamma1_loss=0.000, gamma2_loss=0.000, gamma3_loss=0.000, gamma4_loss=0.000, t0_loss=0.0419, D_loss=0.0307, mean_loss=0.0121]
 
 
 Notice that the loss on the boundaries of the spatial domain is exactly
@@ -263,7 +262,7 @@ Now let’s train with the same configuration as thre previous test
 
 .. parsed-literal::
 
-    Epoch 999: : 1it [00:00, 48.54it/s, v_num=1, mean_loss=1.48e-8, D_loss=8.89e-8, t0_loss=0.000, gamma1_loss=2.06e-15, gamma2_loss=0.000, gamma3_loss=2.1e-15, gamma4_loss=0.000]
+    Epoch 0: : 0it [00:00, ?it/s]Epoch 999: : 1it [00:00, 52.10it/s, v_num=1, gamma1_loss=1.97e-15, gamma2_loss=0.000, gamma3_loss=2.14e-15, gamma4_loss=0.000, t0_loss=0.000, D_loss=1.25e-7, mean_loss=2.09e-8]
 
 .. parsed-literal::
 
@@ -272,7 +271,7 @@ Now let’s train with the same configuration as thre previous test
 
 .. parsed-literal::
 
-    Epoch 999: : 1it [00:00, 43.25it/s, v_num=1, mean_loss=1.48e-8, D_loss=8.89e-8, t0_loss=0.000, gamma1_loss=2.06e-15, gamma2_loss=0.000, gamma3_loss=2.1e-15, gamma4_loss=0.000]
+    Epoch 999: : 1it [00:00, 45.78it/s, v_num=1, gamma1_loss=1.97e-15, gamma2_loss=0.000, gamma3_loss=2.14e-15, gamma4_loss=0.000, t0_loss=0.000, D_loss=1.25e-7, mean_loss=2.09e-8]
 
 
 We can clearly see that the loss is way lower now. Let’s plot the