added DP control, small update of PINN code

diffphys-examples.md

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+Complex Examples with DP
+=======================
+
+The following two sections with show code examples of two more complex cases that 
+will show what can be achieved via differentiable physics training.
+
+First, we'll show a scenario that employs deep learning to learn the erros
+of a numerical simulation, following Um et al. {cite}`um2020sol`.
+This is a very fundamental task, and requires the learned model to closely
+interact with a numerical solver. Hence, it's a prime example of 
+situations where it's crucial to bring the numerical solver into the 
+deep learning loop.
+
+Next, we'll show a tough inverse problem, namely the long-term control
+of a fluid simulation, following Holl et al.  {cite}`holl2019pdecontrol`. 
+This task requires long term planning,
+and hence needs two networks, one to _predict_ the evolution, 
+and another one to _act_ to reach the desired goal. 
+
+Both cases require quite a bit more resources than the previous examples, so you 
+can expect these notebooks to run longer (and it's a good idea to use the checkpointing
+mechanisms when working with these examples).
+