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added DP control, small update of PINN code

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@@ -17,4 +17,6 @@ achieved via DP approaches it is nonetheless a good time to summarize the pros a
- not compatible with all simulators (need to provide gradients)
- require more heavy machinery (in terms of framework support) than previously discussed methods
Especially the last point is one that is bound to strongly improve in a fairly short time, but for now it's important to keep in mind that not every simulator is suitable for DP training out of the box. Hence, in this book we'll focus on examples using phiflow, which was designed for interfacing with deep learning frameworks. Next we can target more a complex scenarios involving fluids.
Especially the last point is one that is bound to strongly improve in a fairly short time, but for now it's important to keep in mind that not every simulator is suitable for DP training out of the box. Hence, in this book we'll focus on examples using phiflow, which was designed for interfacing with deep learning frameworks.
Next we can target more some complex scenarios to showcase what can be achieved with differentiable physics.