Progress Report III (Pres)

Almambet Iskakov, Robert Pienta

12 00 AM ,Mon, Nov 23 2015

Quick Recap

  • Reduce our correlations to only the essential

  • Truncate the 2-pt statistics

  • Study our transient data

  • Use time-varying regression to model our simulation data

  • Reduce our correlations to only the essential ✓

  • Truncate the 2-pt statistics ✓

  • Study our transient data ✓

  • Use time-varying regression to model our simulation data

steady

Reducing Correlations

  • Only two sets of correlations are dependent, clearly the model doesn’t need all six
  • This doesn’t mean all pairs of correlations will work equally!
  • We ran our entire pipeline on combinations of two correlations to see which perform the best
  • This was computationally expensive, but still feasible to do with just 6 choose 2 combinations. r2
  • Ag-Ag and Al-Al performed the best with around 0.74
  • Al-Al and Ag-Cu performed very close with around 0.72 transient

Truncating statistics

  • Truncation based of average 2-pt statistics in each sample in steady state
  • Al = Green, Ag2Al = Orange, and Al2Cu = Blue

Choosing a vector size

vector_size

Example for autocorrelation

horizontal_auto

All steady state data

combined_violation

A New Pipeline

  • We created a whole new pipeline to perform our transient data linkages.
  • Its more than 100x more expensive than the previous pipeline

workwork

Transient Data

PCA components of a single simulation over time transient

  • Wild oscillations until the early 100s

Here are just the first 100 points plotted out: transient

A sanity check of our correlation pair from earlier transient

Future Work

  • (Nov) modeling the time-varying behavior of our system (we are close!)
  • (Nov) post about transience
  • (Nov) post about steady state performance
  • (Dec) Final “In Summa” Post
  • (Dec) Final Presentation

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