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merged ferementer sensitivity and uncertainty into one notebook and c… #87

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merged ferementer sensitivity and uncertainty into one notebook and c…
wclaerhout Oct 19, 2026
049091e
Update P7 student notebooks to match the merged fermenter solution
wclaerhout Oct 20, 2026
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711 changes: 711 additions & 0 deletions exercises/solved_notebooks/P7_sens_uncert/fermenter_mutant_sol.jl
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781 changes: 0 additions & 781 deletions exercises/solved_notebooks/P7_sens_uncert/sens_fermenter_monod_sol.jl
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2 changes: 1 addition & 1 deletion exercises/solved_notebooks/P7_sens_uncert/uncert_bitrophic_model_sol.jl
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Original file line number Diff line number Diff line change
@@ -1,5 +1,5 @@
### A Pluto.jl notebook ###
# v0.19.46
# v0.20.28

using Markdown
using InteractiveUtils
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182 changes: 0 additions & 182 deletions exercises/solved_notebooks/P7_sens_uncert/uncert_fermenter_monod_sol.jl
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34 changes: 17 additions & 17 deletions exercises/solved_notebooks/P7_sens_uncert/uncert_intro_sol.jl
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### A Pluto.jl notebook ###
# v0.20.21
# v1.0.3

using Markdown
using InteractiveUtils
Expand Down Expand Up @@ -199,27 +199,11 @@ In uncertainty propagation, we are interested in the effect of uncertainties (or
Below a Turing model is defined that samples values from a normal distribution with a 5% standard deviation. This is our Prior belief of how accurate the estimate of our intial parameters is. Remember that this is similar to what we did in practical 4.
"""

# ╔═╡ 0e0dc268-faaf-462e-a980-9398c6015e02
@model function logistic_deviation()
μ_dev ~ Normal(0.07, 0.0035) # 5% standard deviation
Wf_dev ~ Normal(10, 0.5) # 5% standard deviation
return solve(remake(oprob_log, p = [:Wf => Wf_dev, :μ => μ_dev]), saveat=0.5);
end

# ╔═╡ e34f4b23-ac1b-4b88-b959-65085cef4b4f
md"""
Using the `sample()` function we can sample values from our distribution.
"""

# ╔═╡ 738448dd-d1c9-4fdc-af5d-fad4d91adf38
μ_model = logistic_deviation();

# ╔═╡ 51579212-dd33-42b9-a19e-221ab7630c1a
chain = sample(μ_model, Prior(), 500);

# ╔═╡ 22bf3f50-1909-4a2c-820f-45b87f6bce14
solutions = generated_quantities(μ_model, chain);

# ╔═╡ 616ee1d4-1f56-4c4a-a658-9f9351949f59
md"""
Finally, we can use theses perturbed values to model the effects of these small perturbations on the output of the model.
Expand All @@ -233,6 +217,22 @@ Firstly, solve the original system (Wf = 10 and μ = 0.07) and afterwards we can
# ╔═╡ 62afc495-29c3-4baa-b45a-94b84149d15d
oprob_log = ODEProblem(growth_mod_log, u0_log,tspan, [:μ => 0.07, :Wf => 10]);

# ╔═╡ 0e0dc268-faaf-462e-a980-9398c6015e02
@model function logistic_deviation()
μ_dev ~ Normal(0.07, 0.0035) # 5% standard deviation
Wf_dev ~ Normal(10, 0.5) # 5% standard deviation
return solve(remake(oprob_log, p = [:Wf => Wf_dev, :μ => μ_dev]), saveat=0.5);
end

# ╔═╡ 738448dd-d1c9-4fdc-af5d-fad4d91adf38
μ_model = logistic_deviation();

# ╔═╡ 51579212-dd33-42b9-a19e-221ab7630c1a
chain = sample(μ_model, Prior(), 500);

# ╔═╡ 22bf3f50-1909-4a2c-820f-45b87f6bce14
solutions = generated_quantities(μ_model, chain);

# ╔═╡ 691a8b68-935e-4012-aa25-9cfdc323b1de
osol_log = solve(oprob_log, Tsit5(), saveat = 0.5);

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