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tweaked expressions

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2021-08-17 21:10:55 +02:00
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diffphys-discuss.md
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@@ -14,14 +14,14 @@ additional properties, and summarize the pros and cons.
## Time steps and iterations
When using DP approaches for learning application,
When using DP approaches for learning applications,
there is a lot of flexibility w.r.t. the combination of DP and NN building blocks.
As some of the differences are subtle, the following section will go into more detail
As some of the differences are subtle, the following section will go into more detail.
We'll especially focus on solvers that repeat the PDE and NN evaluations multiple times,
e.g., to compute multiple states of the physical system over time.
**XXX**
To re-cap, this is the previous figure illustrating NNs with DP operators.
Here, these operators look like a loss term: they typically don't have weights,
To re-cap, here's the previous figure about combining NNs and DP operators.
In the figure these operators look like a loss term: they typically don't have weights,
and only provide a gradient that influences the optimization of the NN weights:
```{figure} resources/diffphys-shortened.jpg
@@ -37,7 +37,7 @@ Similar to the previously described _physical losses_ (from {doc}`physicalloss`)
**Switching the Order**
However, with DP, there's no real reason to be limited to this setup. E.g., we could imagine to switch the NN and DP components, giving the following structure:
However, with DP, there's no real reason to be limited to this setup. E.g., we could imagine a swap of the NN and DP components, giving the following structure:
```{figure} resources/diffphys-switched.jpg
---