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Hello,
first of all I am not sure whether I am posting this to the right place..
I wanted to try use DiffEqFlux.jl to train a ODE system similar to 1st example
but with an outside effect (somethi…
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Tested here: https://github.com/jessebett/DiffEqFlux.jl/blob/master/test/solver_options.jl
If you call `neural_ode` with `save_start=false` it still returns the start. I believe this needed to be o…
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I would like to have the following feature:
It should be possible to add names to the footnote, like
``` text
Aufbauend auf dem Tutorial können wir in weiteren Treffen über aktuell führende
Netze, w…
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I tried with tf-nightly and tfp-nightly (the tfp release does not yet include ode), and the ode solver is not differentiable with gradient tapes. When trying to differentiate the result of the solver,…
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#### Issue Description
I took a stab at implementing neural ODEs described in this paper:
https://arxiv.org/pdf/1806.07366.pdf
I'm having some issues which I think is due to the fact that to im…
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This line [here](https://github.com/JuliaDiffEq/OrdinaryDiffEq.jl/blob/master/src/perform_step/rkc_perform_step.jl#L19) will throw a DomainError when the expression in the square root is negative.
…
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Sir, another issue arises at the last cell, probably last issue!
`ValueError Traceback (most recent call last)
in ()
20
21 f1_img = F1score(result, …
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I wanted to ask you two questions about adaptive computation :)
1. Could you clarify whether the following is how neural ODEs adapt the amount of computation?
Judging by the following code snippe…
ghost updated
5 years ago
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I am trying to use `NeuralNetDiffEq` to solve a PDE.
I am not sure what `X.data` means when defining the volatility function `σ`
For instance, in the first Black-Scholes-Barenblatt example, we hav…
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**Describe the feature and the current behavior/state.**
layers are super fun to write and use and can be quite powerful
**Relevant information**
- Are you willing to contribute it (yes/no): yes
…