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Hi,
thank you for the excellent work.
I have two questions regarding the ODEFunc and NODEBlock/Warper codes.
1. The argument `t` in the `forward` function of `ODEFunc` is not used. But [line…
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Hello,
is there a way to incorporate parameters in the NeuralODE? I want to use the NeuralODE as a surrogate model, that gives me the trajectory of problem at specific time points, depending on the…
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```julia
using DiffEqFlux, OrdinaryDiffEq, Flux, Optim, Plots
u0 = Float32[2.0; 0.0]
datasize = 30
tspan = (0.0f0, 1.5f0)
tsteps = range(tspan[1], tspan[2], length = datasize)
function trueO…
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These prints don't look very good:
```julia
julia> using DiffEqFlux, Flux
julia> NeuralODE(Dense(2,2), (0f0,1f0))
(::NeuralODE{Dense{typeof(identity), Matrix{Float32}, Vector{Float32}}, Vector{F…
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I'd like to send to gpu a NeuralODE object (embedded neural network included), instead of creating a NeuralODE out of a model already on gpu. Below an example:
```julia
julia> using DiffEqFlux, Fl…
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Hi everyone,
It hase been a while that I have an issue with a PINN model. When using a chain of dense layers, the model works fine. However, when using a chain of DENSE and LSTM layers, there seems t…
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I want to create a custom model, that holds two Lux.Chains within. The following Code demonstates this:
```julia
using ComponentArrays,Zygote,Lux,StableRNGs,ForwardDiff
# Create reproducible rn…
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MWE:
```julia
using OrdinaryDiffEq, Zygote
using DiffEqSensitivity, Test, ForwardDiff
p = rand(3)
function dudt(u, p, t)
u .* p
end
function loss(p)
prob = ODEProblem(dudt, [3.0…
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Hello everyone,
I'm currently in the process of training a NeuralODE model using data obtained from an ordinary differential equation (ODE) that describes the underlying physical phenomenon. In my …
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I'm playing around with NeuralODEs and adjoints right now, starting from [the official Lux.jl "MNIST Classification using Neural ODEs" tutorial](https://lux.csail.mit.edu/dev/tutorials/intermediate/1_…