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add coupledSDEs type #212
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30d0970
add coupledSDEs type
oameye a11ba2c
add CoupledSDEs docs
oameye 62c5e7e
apply suggested format changes
oameye 89225a4
add idfunc
oameye f169386
Fix docs?
oameye 43ad5e9
change coupled sde example to use trajectory
oameye 8de87b8
specify which CoupledODEs docstring
oameye 023ff0c
change to SciML default tolerances
oameye f6a400e
classify noise
oameye 5c5e6dc
correct spelling of invertible
oameye ce7a03e
make noisetype field a namedtuple
oameye 573f18f
Merge branch 'main' into CoupledSDEs
Datseris fba7327
make coupledSDEs an extention
oameye e01bce6
Merge branch 'CoupledSDEs' of https://github.com/oameye/DynamicalSyst…
oameye c875b5e
export pretty print
oameye b3ccb26
export CoupledODEs
oameye b08fd7c
add CoupledODEs <-> CoupledSDEs tests
oameye dfc3053
add extention to modules in makedocs
oameye daca4a7
define dynamics rule sde specific
oameye 73af990
port docs into the docstring
Datseris 92e3369
delete ported documentation
Datseris 548130d
fix small typos in docstring
oameye 1a0c884
add CoupledSDEs examples to docs
oameye 413a25a
remove noise strength
oameye 381820f
update docs with no noise strength
oameye cb956e8
add covariance kwarg and change g to kwarg
oameye 17cf5cb
update docs
oameye ba148d9
diffusion <-> covariance matrix
oameye eb8544b
add noise_strength kwarg
oameye e28b4dd
estimate invertiability with a tolerance
oameye 39057fb
move to SOSRA with non-adaptive timesteps
oameye 400ac12
Merge branch 'main' into CoupledSDEs
oameye 41c900b
Reyk's comments
oameye e5bc61a
implemented review comments - part 1
reykboerner f907929
revised CoupledSDEs docstring
reykboerner 75996be
remove broken docs link
reykboerner 7189029
implemented review comments - part 2
reykboerner 109e7b4
proofread and revised CoupledSDEs docs
reykboerner 5df534a
fixed bug that made tests fail
reykboerner 78f25ee
Update CHANGELOG.md
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Merge branch 'main' into CoupledSDEs
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Update Project.toml
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@@ -6,4 +6,5 @@ deps/build.log | |
Manifest.toml | ||
*style.jl | ||
*.scss | ||
*.css | ||
*.css | ||
.vscode |
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[deps] | ||
CairoMakie = "13f3f980-e62b-5c42-98c6-ff1f3baf88f0" | ||
DiffEqNoiseProcess = "77a26b50-5914-5dd7-bc55-306e6241c503" | ||
Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4" | ||
DocumenterTools = "35a29f4d-8980-5a13-9543-d66fff28ecb8" | ||
DynamicalSystemsBase = "6e36e845-645a-534a-86f2-f5d4aa5a06b4" | ||
SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf" | ||
StateSpaceSets = "40b095a5-5852-4c12-98c7-d43bf788e795" | ||
StochasticDiffEq = "789caeaf-c7a9-5a7d-9973-96adeb23e2a0" |
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# `CoupledSDEs` | ||
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```@docs | ||
CoupledSDEs | ||
``` | ||
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## [Examples defining stochastic dynamics](@id defining-stochastic-dynamics) | ||
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Let's look at some examples of the different types of stochastic systems that can be defined. | ||
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For simplicity, we choose a slow exponential growth in 2 dimensions as the deterministic dynamics `f`: | ||
```@example type | ||
using DynamicalSystemsBase, StochasticDiffEq, DiffEqNoiseProcess | ||
using CairoMakie | ||
import Random # hide | ||
Random.seed!(10) # hide | ||
f!(du, u, p, t) = du .= 1.01u # deterministic part | ||
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function plot_trajectory(Y, t) | ||
fig = Figure() | ||
ax = Axis(fig[1,1]; xlabel = "time", ylabel = "variable") | ||
for var in columns(Y) | ||
lines!(ax, t, var) | ||
end | ||
fig | ||
end; | ||
``` | ||
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### Additive noise | ||
When $g$ is independent of the state variables $u$, the noise is called additive, otherwise it is called multiplicative. The ladder is somewhat misleading, as it has come to signify the general case, despite seeming to suggest the limited case where $g(x) \propto x$. We can define a simple additive noise system as follows: | ||
```@example type | ||
sde = CoupledSDEs(f!, zeros(2)); | ||
``` | ||
which is equivalent to | ||
```@example type | ||
t0 = 0.0; W0 = zeros(2); | ||
W = WienerProcess(t0, W0, 0.0) | ||
sde = CoupledSDEs(f!, zeros(2); noise_process=W); | ||
``` | ||
We defined a vector of random numbers `dW`, whose size matches the output of `g`, with the noise applied element-wise, i.e., `g.*dW`. Specifically, `dW` is a vector of Gaussian noise processes whose size matches the output of `g`. The noise is applied element-wise, i.e., `g.*dW`. By default, the noise processes are uncorrelated, meaning the covariance matrix is diagonal. This type of noise is referred to as diagonal. The vector `dW` is by default zero mean white gaussian noise $\mathcal{N}(0, \text{d}t)$ where the variance is the timestep $\text{d}t$ unit variance (Wiener Process). We can sample a trajectory from this system using the `trajectory` also used for the deterministic systems: | ||
```@example type | ||
tr = trajectory(sde, 1.0) | ||
plot_trajectory(tr...) | ||
``` | ||
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#### Correlated noise | ||
Correlated noise is where the random number generated in `dW` are correlated. This can be done by specifying the covariance matrix $\Sigma$ to the `covariance` keyword: | ||
```@example type | ||
ρ = 0.3 | ||
Σ = [1 ρ; ρ 1] | ||
diffeq = (alg = LambaEM(), dt=0.1) | ||
sde = CoupledSDEs(f!, zeros(2); covariance=Σ, diffeq=diffeq) | ||
``` | ||
Alternativly, we can parametrise the covariance matrix by defining the diffusion function $g$ ourselves: | ||
```@example type | ||
g!(du, u, p, t) = (du .= [1 p[1]; p[1] 1]; return nothing) | ||
sde = CoupledSDEs(f!, zeros(2), (ρ); g=g!, noise_prototype=zeros(2, 2)) | ||
``` | ||
Here, we had to provide a `noise_prototype` to indicate that the diffusion function `g` will output a 2x2 matrix. | ||
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#### Scalar noise | ||
Scalar noise is where a single random variable is applied to all dependent variables. To do this, one has to give an one dimensional noise process to the `noise` keyword of the `CoupledSDEs` constructor. | ||
```@example type | ||
t0 = 0.0; W0 = 0.0; | ||
noise = WienerProcess(t0, W0, 0.0) | ||
sde = CoupledSDEs(f!, rand(2)/10; noise_process=noise) | ||
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tr = trajectory(sde, 1.0) | ||
plot_trajectory(tr...) | ||
``` | ||
We can see that noise applied to each variable is the same. | ||
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### Multiplicative and non-autonomous noise | ||
Multiplicative noise in the SciML ecosystem is defined as when $g_i(t, u)=a_i u$. However, in the literature the name is sometimes used for when the noise is not-additive, including nonlinear. In general, we can make the noise time- and state-dependent in a similar fashion to the deterministic dynamics. For example, we can define a system with decreasing multiplicative noise as follows: | ||
```@example type | ||
function g!(du, u, p, t) | ||
du .= u ./ (1+t) | ||
return nothing | ||
end | ||
sde = CoupledSDEs(f!, rand(2)./10; g=g!) | ||
``` | ||
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#### Non-diagonal noise | ||
Non-diagonal noise allows for the terms to linearly mixed via `g` being a matrix. Suppose we have two Wiener processes and two dependent random variables such that the output of `g` is a 2x2 matrix. Therefore, we have | ||
```math | ||
du_1 = f_1(u,p,t)dt + g_{11}(u,p,t)dW_1 + g_{12}(u,p,t)dW_2 \\ | ||
du_2 = f_2(u,p,t)dt + g_{21}(u,p,t)dW_1 + g_{22}(u,p,t)dW_2 | ||
``` | ||
To indicate the structure that `g` should have, we can use the `noise_prototype` keyword. Let us define a special type of non-diagonal noise called commutative noise. For this we can utilize the `RKMilCommute` algorithm which is designed to utilise the structure of commutative noise. | ||
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```@example type | ||
σ = 0.25 # noise strength | ||
function g!(du, u, p, t) | ||
du[1,1] = σ*u[1] | ||
du[2,1] = σ*u[2] | ||
du[1,2] = σ*u[1] | ||
du[2,2] = σ*u[2] | ||
return nothing | ||
end | ||
diffeq = (alg = RKMilCommute(), reltol = 1e-3, abstol = 1e-3, dt=0.1) | ||
sde = CoupledSDEs(f!, rand(2)./10; g=g!, noise_prototype = zeros(2, 2), diffeq = diffeq) | ||
``` | ||
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!!! warning | ||
Non-diagonal problems need specific type of solvers. See the [SciML recommendations](https://docs.sciml.ai/DiffEqDocs/stable/solvers/sde_solve/#sde_solve). |
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module StochasticSystemsBase | ||
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using DynamicalSystemsBase: DynamicalSystemsBase, SciMLBase, correct_state, CoupledODEs, | ||
CoupledSDEs, StateSpaceSets, isinplace, _delete, set_parameter!, | ||
set_state!, dynamic_rule, isdeterministic, current_state, | ||
DynamicalSystemsBase, _set_parameter!, u_modified!, | ||
additional_details, referrenced_sciml_prob, DEFAULT_DIFFEQ, | ||
SVector, SMatrix, current_parameters | ||
using SciMLBase: SDEProblem, AbstractSDEIntegrator, __init, SDEFunction, step! | ||
using StochasticDiffEq: SOSRA | ||
using LinearAlgebra | ||
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include("src/CoupledSDEs.jl") | ||
include("src/classification.jl") | ||
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export CoupledSDEs, CoupledODEs | ||
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end |
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