# Parallelize GSA on continuous outputs

**URL:** <https://discourse.julialang.org/t/parallelize-gsa-on-continuous-outputs/95964>\
**Category:** Modelling & Simulations\
**Tags:** diffeq\
**Created:** [March 12, 2023, 4:15pm UTC](https://discourse.julialang.org/t/parallelize-gsa-on-continuous-outputs/95964 "2023-03-12T16:15:15Z")\
**Posts on this page:** 1\
**Showing post:** 3

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**Author:** ![xztt](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/xztt/32/33843_2.png) [@xztt](https://discourse.julialang.org/u/xztt)\
**Post date:** [March 12, 2023, 5:41pm UTC](https://discourse.julialang.org/t/parallelize-gsa-on-continuous-outputs/95964/3 "2023-03-12T17:41:24Z")

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Yes the example is discrete, however I am looking for SA on the time series outputs of sol[1,:] and sol[2,:]. I am just wondering how I could adapt the above example to incorporate this.

> [@Time Series sensitivity analysis for multiple outputs](https://discourse.julialang.org/t/time-series-sensitivity-analysis-for-multiple-outputs/80631):
>
> Hi, function circ\_local\_time(x) \_prob = remake(prob,u0=x[1:3],p=x[4:end]) sol = solve(\_prob,Tsit5(), reltol=1e-6, abstol=1e-6,saveat=savetime) sol[LV.p] end St = ForwardDiff.jacobian(circ\_local\_time,[u0;p]) x = LinRange(0,1,100) plot(x,St[:,12]) When I run the above code I can see how the sensitivity of the parameter changes during a cycle. However, when I add another output eg function circ\_local\_time(x) \_prob = remake(prob,u0=x[1:3],p=x[4:end]) sol = solve(\_prob,Tsit5()…

I am looking for something similar to what is discussed in the above link. The only difference being I use GSA and it’s run in parallel.

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