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@ -139,16 +139,16 @@ of deep learning and numerical simulation methods.
## More specifically
_Physics_ is a huge field, and we can't cover everything here...
_Physical simulations_ are a huge field, and we won't cover all possible types of physical models and simulations in the following.
```{note} The focus of this book lies on:
```{note} Rather, the focus of this book lies on:
- _Field-based simulations_ (no Lagrangian methods)
- Combinations with _deep learning_ (plenty of other interesting ML techniques, but not here)
- Experiments as _outlook_ (replace synthetic data with real-world observations)
- Experiments as _outlook_ (i.e., replace synthetic data with real-world observations)
```
It's also worth noting that we're starting to build the methods from some very
fundamental steps. Here are some considerations for skipping ahead to the later chapters.
fundamental building blocks. Here are some considerations for skipping ahead to the later chapters.
```{admonition} Hint: You can skip ahead if...
:class: tip
@ -163,8 +163,8 @@ A brief look at our _notation_ in the {doc}`notation` chapter won't hurt in both
## Implementations
This text also represents an introduction to a wide range of deep learning and simulation APIs.
We'll use popular deep learning APIs such as _pytorch_ and _tensorflow_, and additionally
give introductions into _phiflow_ for simulations. Some examples also use _JAX_. Thus after going through
We'll use popular deep learning APIs such as _pytorch_ [https://pytorch.org](https://pytorch.org) and _tensorflow_ [https://www.tensorflow.org](https://www.tensorflow.org), and additionally
give introductions into the differentiable simulation framework _phiflow_ [https://github.com/tum-pbs/PhiFlow](https://github.com/tum-pbs/PhiFlow). Some examples also use _JAX_ [https://github.com/google/jax](https://github.com/google/jax). Thus after going through
these examples, you should have a good overview of what's available in current APIs, such that
the best one can be selected for new tasks.