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Merge branch 'master' of github.com:JuliaTeachingCTU/Scientific-Programming-in-Julia
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docs/Manifest.toml

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docs/Project.toml

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DocumenterTools = "35a29f4d-8980-5a13-9543-d66fff28ecb8"
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DrWatson = "634d3b9d-ee7a-5ddf-bec9-22491ea816e1"
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EcosystemCore = "3e0d8730-8ea0-4ee2-afe6-c85384c618a2"
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GraphRecipes = "bd48cda9-67a9-57be-86fa-5b3c104eda73"
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IRTools = "7869d1d1-7146-5819-86e3-90919afe41df"
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MacroTools = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
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Measurements = "eff96d63-e80a-5855-80a2-b1b0885c5ab7"
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Scientific_Programming_in_Julia = "ff288558-a322-4c39-84bc-85bf6acf7e03"

docs/make.jl

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lecture_13 = [
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"Lab" => "./lecture_13/lab.md"
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"Lecture" => "./lecture_13/lecture.md"
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docs/src/lecture_12/lab.md

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# Lab 12 - Differential Equations
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# [Lab 12 - Differential Equations](@id lab12)
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In this lab you will implement a simple solver for *ordinary differential
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equations* (ODE) as well as a less verbose version of the `GaussNum`s that were

docs/src/lecture_12/lecture.md

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# Uncertainty Propagation in Ordinary Differential Equations
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# [Uncertainty Propagation in Ordinary Differential Equations](@id lec12)
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Differential equations are commonly used in science to describe many aspects of the physical world, ranging from dynamical systems and curves in space to complex multi-physics phenomena.
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- goes through ODEProblem ``x0`` and ``θ`` fields and checks their types
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- replaces GaussNums in ODEProblem by ordinary numbers
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- remembers indices of GaussNum in ``x0`` and ``θ``
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- copies standard deviations in GaussNum to ``sqΣ0``
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- copies standard deviations in GaussNum to ``sqΣ0``

docs/src/lecture_13/lab.jl

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