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