Siddharth manay chandrika kamath center for applied scientific computing 2 march 2005
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Siddharth Manay Chandrika Kamath Center for Applied Scientific Computing 2 March 2005. Progress Report on Data Analysis Work at LLNL: Aug’04 - Feb’05.

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Siddharth Manay Chandrika Kamath Center for Applied Scientific Computing 2 March 2005

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Siddharth manay chandrika kamath center for applied scientific computing 2 march 2005

Siddharth ManayChandrika KamathCenter for Applied Scientific Computing2 March 2005

Progress Report on Data Analysis Work at LLNL: Aug’04 - Feb’05

UCRL-PRES-209947-DRAFT This work was performed under the auspices of the U.S. Department of Energy by University of California Lawrence Livermore National Laboratory under contract No. W-7405-Eng-48.

http://www.llnl.gov/casc/sapphire/


Our progress on earlier applications

Our progress on earlier applications

  • Feature selection for EHOs (data from DIII-D)

    • IDL code + instructions transferred to [email protected]

    • visit to GAT + talk

    • interest in licensing Sapphire software

    • sample scenario ready for the web

  • Separation of signals in climate data

    • a standalone C++ code available which uses our libraries for PCA/ICA

    • to be used in illustrating creation of workflows

    • sample scenario ready for the web

Work done by Erick Cantu-Paz, Imola K. Fodor, Abel Gezahegne, Nu Ai Tang


New application tracking in nstx data

New application: tracking in NSTX data

  • Joint work with PPPL (Klasky)

  • Problem: track the plasma over time

  • IDL code implementing a variant of block matching is too slow

  • Prototyping other block-matching approaches

National Spherical

Torus Experiment

Leveraging LDRD funding (CK); work done by Erick Cantu-Paz, Cyrus Harrison


New application classification of puncture poincar plots for ncsx

New application: classification of puncture (Poincaré) plots for NCSX

  • Joint work with PPPL (Klasky, Pomphrey, Monticello)

  • Classify each of the nodes: quasiperiodic, islands, separatrix

  • Connections between the nodes

  • Want accurate and robust classification, valid when few points in each node

National Compact

Stellarator Experiment

Quasiperiodic

Islands

Separatrix


Siddharth manay chandrika kamath center for applied scientific computing 2 march 2005

Piecewise Polynomial Models for Classification of Puncture Plots


Polar coordinates

Polar Coordinates

  • Transform the (x,y) data to Polar coordinates (r,).

  • Advantages of polar coordinates:

    • Radial exaggeration reveals some features that are hard to see otherwise.

    • Automatically restricts analysis to radial band with data, ignoring inside and outside.

    • Easy to handle rotational invariance.


Piecewise polynomial fitting dividing data into intervals

Piecewise Polynomial Fitting: Dividing data into intervals.

  • Use the q-histograms to find intervals.

  • Need to divide the q domain into intervals that are:

    • Restricted to regions of q that have data.

    • Small enough so that polynomial will fit the data.

    • Large enough to span gaps where data is missing


Piecewise polynomial fitting computing polynomials

Piecewise Polynomial Fitting: Computing polynomials

  • In each interval, compute the polynomial coefficients to fit 1 polynomial to the data.

  • If the error is high, split the data into an upper and lower group. Fit 2 polynomials to the data, one to each group.

Blue: data.Red: polynomials. Black: interval boundaries.


Classification

Classification

  • The number of polynomials needed to fit the data and the number of gaps gives the information needed to classify the node:

2 Polynomials

2 Gaps

 Islands

2 Polynomials

0 Gaps

 Separatrix


Results

Results

3995 points, Separatrix

250 points, 3 Islands

Puncture 1, node 79

Zoom around =1.6

Zoom around =1.6


Future work

Future work

  • Set up web pages for climate and fusion scenarios

  • NSTX data: continue building and testing block-matching algorithms

  • NCSX data

    • continue interactions with Neil, Don, Scott

    • continue to refine and validate approach

    • investigate ways of making it more robust

    • investigate exploiting nearby nodes

    • design and implement in C++ for insertion into PPPL analysis pipeline


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