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Simulate a stochastic differential equation?


Hello there

I need to reproduce the results of a simulation found elsewhere. The model is a set of nonlinear differential equations with 5 variables (x1,x2,z,y1,y2). To the equations on the derivatives of x1 and y1 I need to add a Gaussian noise with standard deviation set to 0.025. To the ones on x2 and y2, 0.25.
Something like: dx1/dt=fx1(x1,x2,y1,y2,z)+WGN(0,0.025), dx2/dt=fx2(x1,x2,y1,y2,z)+WGN(0,0.25)
and so on

Could someone help me out with directions, methods to be used and etc, please?

Thank you.


POSTED BY: Eduardo Mendes
10 days ago


there are different ways of writing this. Here is an intuitive one (but not the most elegant):

(*Assuming the noise is independent for the processes*)
cW[\[Rho]_] := ItoProcess[{{0, 0}, IdentityMatrix[2]}, {{w1, w2}, {0, 0}}, t, {{1, \[Rho]}, {\[Rho], 1}}];
procGBMnoiseM = ItoProcess[{\[DifferentialD]S[t] == \[Mu][t] S[t]  \[DifferentialD]t + \[Sigma] S[t] \[DifferentialD]Subscript[w, s][t], \[DifferentialD]\[Mu][t] == \[Lambda]*\[Mu][t]*(\[Mu]base - \[Mu][t]) \[DifferentialD]t + \[Sigma]2*\[DifferentialD]Subscript[w, r][t]}, {S[t], \[Mu][t]}, {{S, \[Mu]}, {0.01, \[Mu]base}},t, {Subscript[w, s], Subscript[w, r]} \[Distributed] cW[\[Rho]]];

This can be plotted like so:

ListLinePlot[RandomFunction[procGBMnoiseM /. { \[Sigma] -> 0.3, \[Sigma]2 -> 0.01, \[Lambda] -> 0.1, \[Mu]base -> 0.1, \[Rho] -> 0.0}, {0, 40, 0.01}], ImageSize -> Large, AxesLabel -> {"time", "Share Price"}, PlotLabel -> "Share Price"]

enter image description here

If you want more processes this can easily be generalised, of course. Here is a sort of "stochastic Kuramoto model", well sort of:

(*M is the number of trading partners, i.e. countries, \[Epsilon] is \
the strength of the interaction. The average trade is a number that \
describes the "normal/default trading volume of the country". *)
M = \
30; \[Epsilon] = 0.5; averagetrade = 
 Table[RandomReal[{1, 5}], {i, 1, M}]; Do[
 Subscript[\[Omega], i] = 
  RandomVariate[NormalDistribution[1., 0.025]], {i, 1, M}];
(*Obviously the adjacency matrix describes the trade network *)

adjacencymatrix = RandomChoice[{0.7, 0.3} -> {0, 1}, {M, M}];
(*The Ito process does not allow us to use variables with indices \
such as Subscript[x, i] ; this is why I generate strings for the \
variable names*)

variables = ToExpression @ Table["x" <> ToString[i], {i, 1, M}];
(*Here I generate the deterministic part of the equations. I assume a \
basic logistic dynamics (with the "carrying capacity" representing \
the averate trade value of the countries. I couple the countries \
diffusively similar to the Kuramoto system.*)

eqnsrhsdet = 
    i] + \[Epsilon]/M Sum[
      adjacencymatrix[[j, i]]*(variables[[j]][t] - 
         variables[[i]][t]), {j, 1, M}], {i, 1, M}];
(*This is \[Sigma] matrix of the noise. All noise terms are assumed \
to be independent. This is why this is a diagonal matrix. One could \
make the fluctuations of the trade of individual countries different \
by not using 0.3 everywhere on the diagonal.*)

eqnsrhsstoch = 0.4 IdentityMatrix[M];
(*Here we set initial conditions*)

initconds = Table[2 \[Pi] RandomReal[], {i, 1, M}] :
    (*\[Mu] quantifies how quick countries want to return to their \
default trading.*)
    proc = 
   ItoProcess[{eqnsrhsdet, eqnsrhsstoch}, {variables, initconds}, {t, 
(*We generate the process. Time is from 0 to 10 and the sampling rate \
is 0.1*)
tseries = RandomFunction[proc, {0., 100., 0.1}, 1];
(*Just a wee plot*)
data = Mod[#, 2 \[Pi]] & /@ 
   Table[tseries["PathComponents"][[k]]["Paths"][[1, All, 2]], {k, 1, 
ListLinePlot[data[[1 ;; 4]], ImageSize -> Large]

enter image description here

Please be aware that the comments refer to trade and different countries. This is quite a bit out of the context of where it was presented. So the model has a very (!) loose relationship (if at all) to trading. The trading example was rather used as an analogy to motivate the terms.



POSTED BY: Marco Thiel
9 days ago

Hi Marco

Thank you very for the answer but unfortunately I could not figure out how to adapt your code to my problem. If it is not asking too much, I am sending below the set of equations for you to see. Noise needs to be added to the first, second, fourth and fifth equations. The set of stochastic equations was solved using Euler-Maruyama method in the original paper (10.1093/brain/awu147).

enter image description here

Thank you very much


POSTED BY: Eduardo Mendes
6 days ago

Can you show me the code that you tried? It seems to be a simple typing exercise; and I am a friend of people typing in their own equations. I might be able to help if you show me your code.

Best wishes, Marco

POSTED BY: Marco Thiel
6 days ago

Group Abstract Group Abstract