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This is `f!(dx, x, p, t)` (in-place version) and `f(x, p, t)` (out-of-place version). It is the "canonical" function signature in DifferentialEquations.jl
We can call this internally a `NonlinearCo…
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https://github.com/JuliaDiffEq/StochasticDiffEq.jl/pull/53
@onoderat 's Pr introduces `ggprime` which is `g` times the Jacobian of `g`. This is defined in the PCE paper (see https://github.com/Juli…
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I was wondering if you guys would think it's necessary to support arbitrary sized inputs. For example, instead of just allowing matrix vector, allow matrix matrix in the normal interpretation. This ca…
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It would be convenient if we can do `solve(prob)` without loading the whole DifferentialEquations. I think it can be done with, for example, something like this:
```julia
module OrdinaryDiffEq
…
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First of all, really nice work!
As noted in the DifferentialEquations plan for modularization (https://github.com/JuliaDiffEq/DifferentialEquations.jl/issues/59), the only form of "conditional depend…
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```jl
using DiffEqNoiseProcess
#using DifferentialEquations
u0 = rand(2)
tgrid = 0.0:0.1:10.0
brownian_noise = randn(length(u0)*(length(tgrid)-1))
brownian_noise = reshape(brownian_noise,length(…
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Run the code to reproduce the error
```julia
using ModelingToolkit, DifferentialEquations, Plots
# Define our state variables: state(t) = initial condition
@variables t x(t)=1 y(t)=1 z(t)=2
#…
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When using the _lyapunovspectrum_ function, it would be nice to use the Jacobian's structure (diagonal, banded, rank1, and so on) especially as long as one doesn't use a GPU for the matrix multiplicat…
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Reproducer
```julia
using DifferentialEquations, IterativeSolvers, LinearSolve
function lorenz!(du,u,p,t)
du[1] = 10.0*(u[2]-u[1])
du[2] = u[1]*(28.0-u[3]) - u[2]
du[3] = u[1]*u[2]…
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`ode23s` fails with 2D matrices as the y0. Here's the traceback:
![](https://files.gitter.im/JuliaODE/ODE.jl/Capture.PNG)
The code is generated from DifferentialEquations.jl's wrapper. I'll be makin…