pbdl-book/diffphys-examples.md

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).