julialang

Hello again!

Now I try to calculate the Lyapunov spectrum in a two-parameter plane. But a warning comes when I choose some of the parameter regions:

Warning: Instability detected. Aborting

From what I googled, the warning comes because it returns a NAN in the trajectory. I am thinking if I can use the 'isnan' function to skip those parameter sets that cause the NAN and continue my

I think our walk!(agent, rand, model) is an unintuitive function, with also less power than possible. When I think of a random walk, I would think that I provide a radius to a function, and the agent takes a step with distance as much as the radius, but random direction.

Our function doesn't allow for this. So, I propose that we implement a randomwalk! function with specification:

I am unable to port my kernels to use KernelAbstraction.jl since CUDADevice is not defined after importing. eg.

using CUDA
using KernelAbstractions

CUDA.functional()
> true

device = CUDADevice()
> ERROR: UndefVarError: CUDADevice not defined

Note that CUDA works just fine. The versions I am currently using are: CUDA: 3.8.1 and KernelAbstractions: 0.8.0

We should be able to attach names to rules, so that normalization steps (and error messages?) can be better understood by users. For example:

normalize(@term(diff(x + y, x)))
 - @term(diff(x,x) + diff(y,x)) by sum rule in differentiation
 - @term(one(x) + diff(y, x)) by linear rule of differentiation
 - @term(1 + diff(y, x)) by multiplicative identity of a number
 - @term(1 + zero(x))