Outline
 Meeting with domain experts outcome
 Image segmentation revisited

Chord length distribution expanded
 PCA input plots
 PCA output plots
 Preliminary regression results
 What’s next?
Outcome from Meeting with Domain Experts

Meeting with a computational scientist in the area of multiscale modeling @ Sandia National Lab.

Extremely useful meeting after which we added 2 more classes to the current simulation pool we had before.
Images of New Classes Added
Rolling:
Images of New Classes Added
Random Distribution with Random Shapes:
Obtaining Grain Boundary and Precipitates Revisited
Obtaining Grain Boundary and Precipitates Revisited
 Image of old image segmentation algorithm
Obtaining Grain Boundary and Precipitates Revisited
 Image of new image segmentation algorithm
Chord Length Distribution Extraction Expanded
 First decided to not use the average Chord Length distribution.
 We normalized the distribution such that the area under the curve is equal to 1.
 At this point this chord length tells us the probability of finding a chord of length “X” within our MS, nevertheless it is not enough.
 We needed to add something that accounts, “weighs” more the chords of bigger size.
Chord Length Distribution Extraction Expanded
 Thus by multiplying the frequency of each chord by its size and normalizing it again, the bigger chords now have a “bigger weight” in the distribution.
PCA Input
 PACE job using 64GB of memory
 Data matrix size (
n_simulations
,27000000) wheren_simulations
=220  220 is the total number of simulations we were able to perform during semester
PCA Input Screen Plot
 More than 95% variance captured in first 5 PC values
PCA Output
 Chord length distributions (in general) cleaned up after new segmentation code
 Data matrix size (
n_simulations
, 299) wheren_simulations
=220
PCA Output Screen Plot
 More than 95% variance captured in first 3 PC values
Regression Test
 We now have two
.m
files containing our PC values for both the inputs and outputs of our model  Used “hacked” ScikitLearn/PyMKS module to perform linear regression on our PC values
 Objective: predict chord length distribution given a new precipitate distribution
 We can calculate the
Rsquare
value for a give combination of polynomial degree and number of PC values used Can create plot showing all combinations of a given set of values in
degree
andn_components
 Can create plot showing all combinations of a given set of values in
(Thanks David Brough!)
Regression Test
 Using all simulations (220)
Regression Test Results
 Order of Polynomial: 2
 Number of Components: 3
 Rsquared Value: 0.708516065498
Next Steps
 Further analysis on selecting input and output of our model
 Improve processstructure linkage