ModelsEnvironments

PSE (Pattern Space Exploration) method is used to explore the output's diversity of a model.
Input parameter values are selected to produce new output values, such that as the exploration progresses, the region of
the output space that is covered gets bigger. PSE reveals the potential of your model: the
variety of the dynamics it is able to produce, even those about which you were not investigating in the first place !
**Method scores:**

The PSE method is designed to cover the output space, hence the highest score possible in output exploration. PSe is all about*covering* output space, hence the low scores in optimization and Input Space exploration.
As the methods discovers patterns in the output space, inputs values that lead to these patterns are available, that give litlle insights
about the model sensitivity.
Contrarily to calibration-based methods, PSE is sensitive to the dimensionality of the **output** space, as it maintains an archive
of the output space locations covered ever since, which is rapidly costly for more than 3-4 dimensions.

PSE handles stochasticity in the sense that the selected pattern are estimated by the median of several model execution output values.

oke-m oCalibrg/ion)
PLOS ONE 10(9), 2015.oke-mmmmm[online version]mm[bibteX]
Available methodsCalibrg/ion 5/a>Profiles 5/a>PSE 5/a>Data Processrng 5/a>Other DoEs 5/a>See also Language 5/a>GUI guide 5/a>Advanced Concepts 5/a>

The PSE method is designed to cover the output space, hence the highest score possible in output exploration. PSe is all about

PSE handles stochasticity in the sense that the selected pattern are estimated by the median of several model execution output values.

`genome`

:ethe model2par.60-12s, varyrng2 thinetheir minimumyand maximumybounds,oke-m`objectrves`

:ethe observables3measured2in: each simulation and thinewhich we2search in: diversity,d th a discretization step ,`stochastic`

:ethe seed2generg/or,ewhich generg/ss su table seeds in: the method. Mandg/ory if you2 model2contains randomness. The generg/sd seed2in: the model2taskeis tnejo,0 /sd through the variable give as an argument of Replication (here myseed).```
//seed2declarg/ion in: random numb12egenerg/ion
val myseed =Val[Int]
val explorg/ion =oke-mPSE (130082rgenome =oke-mmmmmSeq(oke-mmmmm 2par.61y8in (0.0, 1.0),oke-mmmmm 2par.628in (-10.0, 10.0)),oke-mmmobjectrves =oke-mmmmmSeq(oke-mmmmm 2output18in (0.02to 40.02bym5.0),oke-mmmmm 2output28in (0.02to 4000.02bym50.0)),oke-mmmstochastic = Stochastic(seed = myseed)oke-m)
val evol"4ion =oke-mSteadyStg/sEvol"4ion(oke-mmmalgor thm = explorg/ion,oke-mmmevalug/ion = modelTask,oke-mmmpar.llelism = 10,oke-mmm-12mination = 100 oke-m)
```

where `par.61`

and `par.62`

are inputo
of the taskethat runs the model, and `output1`

and
`output2`

are outputs of that s.60 task. The numb12
of inputo and outputs are il.5,0 ed.o