
Design of experiments on a space of parameters |
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Calibration |
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Optimization |
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Sensitivity analysis |
It implements exploration algorithms. |
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It transparently delegates computational loads to massively parallel environments out of the box. |
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The exploration transition unrolls a design of experiment.
It creates one execution stream by sample in the design of experiment.
The aggregation transition gathers the results of a model exploration.
It allows to compute global indicators upon multiple parallel execution streams.
val i1 = Prototype[Int]("i1")
val i2 = Prototype[Int]("i2")
val j = Prototype[Int]("j")
val hello = GroovyTask("hello", "j = Model.compute(i1, i2)")
hello addInput i1
hello addInput i2
hello addOutput j
hello addLib "/path/to/model.jar"
val exploration = ExplorationTask(
"exploration",
Factor(i1, 0 to 100 by 2 toDomain) x
Factor(i2, UniformIntDistribution() take 10)
)
val ex = exploration -< (hello by 10 on biomed) toExecution
ex.start
Chromosome structuring |
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C++ |
SimTRAP project |
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NetLogo |
Bioemergence project |
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C / C++ / Python |
Simpop project |
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Scala |







