I have been trying to estimate parameters with one set of data. First, i followed the examples of least squares parameter estimation to write the first code where i generated 3 toy data sets for x, y and z . Everything works fine. function f(du,u,p,t) du[1] = dx = -p[1]*u[1]*u[2] du[2] = dy = …

[image] ak47: cost_function = build_lsoptim_objective(prob,t,y_data,Tsit5()) cost_function = build_lsoptim_objective(prob,t,y_data,Tsit5(),save_idxs = 2) so it’s only saving and comparing against y.

Thanks Chris for quick reply. I tried this and got a dimensions mismatch. Array could not be broadcast to match destination. May be it’s about prob ??

Post the full error message. My guess is that it expects the data to still be a matrix, so save_idxs = 2:2 might be the easy fix, or maybe you have to y_data', but the answer would be in the error message.

Thanks once again. Tried the easy fixes but error still persists. Here is the error report. DimensionMismatch: array could not be broadcast to match destination Stacktrace: [1] check_broadcast_shape @ .\broadcast.jl:540 [inlined] [2] check_broadcast_axes @ .\broadcast.jl:543 [inlined] [3] che…

Thank you Chris. Changed the cost_function to cost_function = build_loss_objective(prob,Tsit5(),L2Loss(t,y_data), maxiters=10000,verbose=false,save_idxs=[2]) and it worked.

Hello, I tried your problem, but I still have error. How did you fix it? The error is as follows: BoundsError: attempt to access 2-element Vector{Float64} at index [3] Stacktrace: [1] setindex! @ ./array.jl:903 [inlined] [2] finite_difference!(f::DiffEqParamEstim.var"#37#42"{Nothing, Bool, In…

Did you change to cost_function = build_loss_objective(prob,Tsit5(),L2Loss(t,y_data), maxiters=10000,verbose=false,save_idxs=[2])?

Do exactly what Chris suggested. Only that optimization was easily achieved using black box optim. I used to find difficult with these optimization problems in Matlab but found them super easy in Julia. Thanks to Chris for his great ordinary differential tutorials.

Yes I did function f(du,u,p,t) du[1] = dx = -p[1]*u[1]*u[2] du[2] = dy = p[1]*u[1]*u[2] - p[2]*u[2] du[3] = dz = p[2]*u[2] end u0 = [738.0;1.0;0] tspan = (0.0,14.0) p = [0.00237,0.465] prob = ODEProblem(f,u0,tspan,p) sol = solve(prob,Tsit5()) t = collect(3:14) y_data = [25,75,227,296,258…

Nonlinear least squares Parameter estimation with one set of data points

New to Julia

rafael.guerra July 13, 2022, 1:31pm 9

Gents, please mind the environment and post smarter your long codes & error messages by using the available Discourse tools.

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Nonlinear least squares Parameter estimation with one set of data points

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