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GEM-SA: a tutorial. John Paul Gosling University of Sheffield. Overview. GEM-SA: Gaussian Emulation Machine for Sensitivity Analysis It’s a Windows based program that has a graphical interface created by Marc Kennedy during his time in CTCD - PowerPoint PPT Presentation
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Slide 1
John Paul Gosling
University of Sheffield
GEM-SA: a tutorial
Slide 2mucm.group.shef.ac.uk
Overview
GEM-SA:Gaussian Emulation Machine for Sensitivity
Analysis It’s a Windows based program that has a
graphical interface created by Marc Kennedy during his time in CTCD
It does emulation for prediction, uncertainty analysis and sensitivity analysis
It also has a facility to create experimental designs for the analysis of computer models.
Slide 3mucm.group.shef.ac.uk
Starting the program
On the desktop, there is a folder <GEM-SA tutorial>, opening it will reveal two other folders:
Inside the folder <GEM-SA1.1> is the program:
Double-clicking this will start the program
Slide 4mucm.group.shef.ac.uk
Main window
menutoolbar
log window
Sensitivity Analysis output grid
Slide 5mucm.group.shef.ac.uk
Generating input designs
There are two designs available: LP-TAU and Maximin Latin Hypercube. Both have good space filling properties.
Press this button to create a file of inputs for your computer model
Slide 6mucm.group.shef.ac.uk
Generating input designs
Then we specify ranges over which the input will be of interest
These must cover your beliefs about the range of each input
Slide 7mucm.group.shef.ac.uk
The design
Here’s a 50-point LP-TAU design for three inputs
You’ll also find they’ve been written to the file you specified (LP_TAU50.txt) in GEM-SA’s working directory
Slide 8mucm.group.shef.ac.uk
Creating/Editing a project
Now, we’ll run through some of the options available to us for emulator building.
We can create a new project or edit an existing project by selecting the appropriate item from the project menu.
Or we can use these toolbar buttons.
New Edit
Slide 9mucm.group.shef.ac.uk
Edit Project - Files
Names of input files
Names of output files
Slide 10mucm.group.shef.ac.uk
Edit Project - Options
How many inputs?
Edit input names
Slide 11mucm.group.shef.ac.uk
Edit Project - Options
What should be calculated, and how?
Which joint effects should be calculated?
Slide 12mucm.group.shef.ac.uk
Edit Project - Options
Are the inputs uncertain?
What prior mean for the output?
Slide 13mucm.group.shef.ac.uk
Edit Project - Options
What kind of predictions and cross validation?
Slide 14mucm.group.shef.ac.uk
Edit Project - Simulations
MCMC control parameters Number of
realisations for prediction and ME/JE
How many points used to calculate main effects, joint effects
Slide 15mucm.group.shef.ac.uk
Input names
By clicking the <Names…> button, a window opens that allows us to name each of the inputs.
This can be handy when viewing the variance decomposition results and main effects plots.
Slide 16mucm.group.shef.ac.uk
Distributions for inputs
When we click the <OK> button, the following window opens.
This windows allows us to specify our beliefs about the inputs.
Slide 17mucm.group.shef.ac.uk
A first run through
Consider the simple nonlinear model we saw earlier
y = sin(x1)/{1+exp(x1+x2)}
We have 2 inputs, x1 and x2, and we assume they both must be valued in the range [0,1].
20 points will give us a decent coverage of the unit square that is the input space here.
Two files have already been saved in the folder <Examples\Eg1> to help save us time.
Slide 18mucm.group.shef.ac.uk
Monte Carlo method Here’s the result of a Monte Carlo analysis
using 30 input pairs.
Mean = 0.139, median = 0.142 Std. dev. = 0.053 Variance = 0.0028
Slide 19mucm.group.shef.ac.uk
Monte Carlo method
Mean = 0.114, median = 0.115 Std. dev. = 0.054 Variance = 0.0029
Here’s the result of a Monte Carlo analysis using 10,000 input pairs.
Slide 20mucm.group.shef.ac.uk
Prediction
Predictions can be Correlated realisations of outputs at the
prediction inputs Similar to main effect outputs
Marginal means and variances of outputs at the prediction inputs
Faster to compute, especially with many prediction points
Easy to interpret
Slide 21mucm.group.shef.ac.uk
A plot of the predictions
Here is the prediction output files plotted with the real function with x2 fixed at 0.5.
Slide 22mucm.group.shef.ac.uk
Cross validation
Choice of none, leave-one-out or leave final 20% out
Leave-one-out Hyperparameters use all data and are then
fixed when prediction is carried out for each omitted point
Leave final 20% out Hyperparameters are estimated using the
reduced data subset
Slide 23mucm.group.shef.ac.uk
A real example
A dynamic vegetation model is being used to predict the NBP of deciduous broadleaf woodland in the vicinity of Whitby, North Yorkshire.
The scientists are uncertain about ten inputs of the model and want to know how this uncertainty affects the NBP output of the model – Monte Carlo methods are out of the question as the model is too complex.
When they used their best guesses for these inputs, the model returned a NBP of 146.4gC/m2.
Slide 24mucm.group.shef.ac.uk
The input names in order
Maximum age (years) N(200,625) Water potential (M Pa) N(3,0.25) Leaf life span (days) N(190,1600) Leaf mortality index N(0.005,6.25e-6) Bud burst limit (degree days) N(135,6.25) Seeding density (m2) N(0.1,0.0001) Soil sand (%) N(43.27,222.12) Soil clay (%) N(22.36,49.21) log(stem growth rate) N(-5.116,0.041209) Bulk density N(1.214,0.0325)
Slide 25mucm.group.shef.ac.uk
Main effects plots
The plug-in estimate of the NBP is far away from our mean for NBP as the main effect plot for bulk density is concave around it’s expected value of 1.214.
Slide 26mucm.group.shef.ac.uk
Producing main/joint effects plots for publication
In the files section of the edit project window, there are two fields that allow the user to specify where the main/joint effects data should be written.
These files can be used to produce graphs like the one I showed earlier.
The main effects file is structured as follows: There are a number of blocks of function
realisations – one for each input. These are controlled by
Slide 27mucm.group.shef.ac.uk
Limitations of GEM-SA
In theory, the methods used by GEM-SA are limitless; however, the program itself isn’t.
It can handle up to 30 inputs and 400 training data.
Also, the distributions that are used to express our uncertainty about the inputs are limited to uniform or normal.
Slide 28mucm.group.shef.ac.uk
When it all goes wrong…
How do we know when the emulator is not working? Large roughness parameters
Especially ones hitting the limit of 99 Large emulation variance on UA mean Poor CV standardised prediction error
Especially when some are extremely large
In such cases, see if a larger training set helps Other ideas like transforming output scale
Slide 29mucm.group.shef.ac.uk
Where to find the program
GEM-SA is available on the web along with tutorial slides from a longer course and further example data sets.
Links to it can be found on my website where there is also a technical report explaining the perils of using the “plug-in” approach:
j-p-gosling.staff.shef.ac.uk