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2021-04-12 09:19:00 +08:00
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diffphys-discuss.md
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@@ -10,7 +10,8 @@ additional properties, and summarize the pros and cons.
![Divider](resources/divider4.jpg)
## Time Steps and Iterations
## Time steps and iterations
When using DP approaches for learning application, there is a large amount of flexibility
w.r.t. combination of DP and NN building blocks.
@@ -61,7 +62,7 @@ Note that this picture (and the ones before) have assumed an _additive_ influenc
DP setups with many time steps can be difficult to train: the gradients need to backpropagate through the full chain of PDE solver evaluations and NN evaluations. Typically, each of them represents a non-linear and complex function. Hence for larger numbers of steps, the vanishing and exploding gradient problem can make training difficult (see {doc}`diffphys-code-sol` for some practical tipps how to alleviate this).
## Alternatives: Noise
## Alternatives: noise
It is worth mentioning here that other works have proposed perturbing the inputs and
the iterations at training time with noise {cite}`sanchez2020learning` (somewhat similar to