From 7d8a4db9d52cb077f6b12d1abeb7a9969b8f265d Mon Sep 17 00:00:00 2001 From: kbarnhart Date: Tue, 7 May 2019 15:33:15 -0600 Subject: [PATCH] update notes --- resources/clinic_notes.txt | 50 ++++++++++++++++++++------------------ 1 file changed, 26 insertions(+), 24 deletions(-) diff --git a/resources/clinic_notes.txt b/resources/clinic_notes.txt index f5ff8b4..6bec21a 100644 --- a/resources/clinic_notes.txt +++ b/resources/clinic_notes.txt @@ -1,39 +1,41 @@ -Intro to calibration +# Intro to calibration - what is calibration/optimization/parameter estimation. -- gradient based vs global -- complex model vs statistical surogate - black box model (parameters > model > outputs) - parameters and outputs must be defined. -Our toy model +- gradient based vs global +- complex model vs statistical surogate +- parameter uncertainty or just best parameter set + +# Our toy model - diffusion of heat + surface temperature history - OF based on fitting Clow paper data. -Intro to Dakota -- Dakota has more bells and whistles, it is well thought out, and the documentation is quite good. Its just extensive and not an iPhone. -- Core activity (assuming you have a black box model set up) is to create and run an input file. -- Look at .in file. +# Intro to Dakota +- Dakota has more bells and whistles, it is well thought out, and the + documentation is quite good. Its just extensive and not an iPhone. +- Core activity (assuming you have a black box model set up) is to create and + run an input file. +- Look at .in file. * discuss each part - Look at template file and driver.py (connect this with black box parts) -- Run Dakota, create plots, look at output. +- Run Dakota, create plots, look at output. - Discuss Dakota's file structure -Other methods -We just did a brute force grid search. -Step 2: Gradient based method -Step 3: EGO +# Other methods +We just did a brute force grid search. This is sort of an optimization. +Next we will do a gradient based method and a global method. -Discussion: -* computational cost of Dakota method vs complex model evaluation. - - calculation of numerical gradients - - increasing dimension - - -* RST file, .out file and reproducible research -* We haven't yet talked about the uncertainty estimates on parameters, just which -parameter is best. That is for another day. +# Discussion: +- computational cost of Dakota method vs complex model evaluation. + * calculation of numerical gradients + * increasing dimension +- do you need parameter estimates, or just a best fit point. +- RST file, .out file and reproducible research +- We haven't yet talked about the uncertainty estimates on parameters, just + which parameter is best. That is for another day. -Exploration if time: +# Exploration if time: * Explore other methods * Add a second component of the objective function. -* Make the model (of surface temperature history) more complex. - +* Make the model (of surface temperature history) more complex.