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Complex Examples Overview
The following sections will give code examples of more complex cases to show what can be achieved via differentiable physics training.
First, we’ll show a scenario that employs deep learning to represent
the errors of numerical simulations, 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 how to let NNs solve tough inverse problems, namely
the long-term control of a Navier-Stokes 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.
(Later on, in {doc}reinflearn-code we will compare this
approach to another DL variant using reinforcement learning.)
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 check-pointing when working with these examples).