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comments on DP naming

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@ -78,12 +78,12 @@ of human understanding. However, these viewpoints typically stem from
relying on hearsay and not dealing with the topic enough.
Rather, the situation is a very common one in science: we are facing a new class of methods,
and "all the gritty details" are not yet fully worked out. However, this is pretty common
for scientific advances.
and "all the gritty details" are not yet fully worked out. This is pretty common
for all kinds of scientific advances.
Numerical methods themselves are a good example. Around 1950, numerical approximations
and solvers had a tough standing. E.g., to cite H. Goldstine,
numerical instabilities were considered to be a "constant source of
anxiety in the future" {cite}`goldstine1990history`.
numerical instabilities were considered to be a
"constant source of anxiety in the future" {cite}`goldstine1990history`.
By now we have a pretty good grasp of these instabilities, and numerical methods
are ubiquitous and well established.
@ -170,6 +170,25 @@ interleaved approaches that leverage _differentiable physics_ allow for
very tight integration of deep learning and numerical simulation methods.
### Naming
It's worth pointing out that what we'll call "differentiable physics"
in the following appears under a variety of different names in other resources
and research papers. The differentiable physics name is motivated by the differentiable
programming paradigm in deep learning. Here we, e.g., also have "differentiable rendering
approaches", which deal with simulating how light leads forms the images we see as humans.
In contrast, we'll focus on _physical_ simulations from now on, hence the name.
When coming from other backgrounds, other names are more common however. E.g., the differentiable
physics approach is equivalent to using the adjoint method, and coupling it with a deep learning
procedure. Effectively, it is also equivalent to apply backpropagation / reverse-mode differentiation
to a numerical simulation. However, as mentioned above, motivated by the deep learning viewpoint,
we'll refer to all these as "differentiable physics" approaches from now on.
---
## Looking ahead
_Physical simulations_ are a huge field, and we won't be able to cover all possible types of physical models and simulations.
@ -203,9 +222,6 @@ the best one can be selected for new tasks.
As we're (in most Jupyter notebook examples) dealing with stochastic optimizations, many of the following code examples will produce slightly different results each time they're run. This is fairly common with NN training, but it's important to keep in mind when executing the code. It also means that the numbers discussed in the text might not exactly match the numbers you'll see after re-running the examples.
---
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<!-- ## A brief history of PBDL in the context of Fluids