Where’s the love?

Our simulation informatics blog hasn’t received much attention recently. I (Paul) have been getting settled in my new position at Colorado School of Mines—learning how to be a professor. David has been busy with his gig at Purdue and his other blog. We’re also both in talks with the folks at SIAM about writing for their upcoming blog; stay tuned for that.

You can stay up-to-date with our latest musings on computational math by following us on Twitter: @DrPaulynomial and @dgleich, respectively.

Model Reduction with MapReduce-enabled Tall and Skinny Singular Value Decomposition

David and I recently submitted a paper on some work we’ve be churning on for a long long time. I’m pretty excited it’s finally submitted. Here’s a link to the preprint on arXiv:

Model Reduction with MapReduce-enabled Tall and Skinny Singular Value Decomposition

Stay tuned for a “Behind the Research” perspective…

Active subspace methods in theory and practice

Qiqi Wang, Eric Dow, and I just submitted our recent work on active subspace methods including a theoretical framework and connections to kriging response surfaces. 

I’m giving talks at the national labs this week telling people about the work. Here’s the link to the arXiv paper:

Active subspace methods in theory and practice: Applications to kriging surfaces

Emily Shuckburgh

At SIAM Annual this year, Emily Shuckburgh gave a really great talk about simulation informations in the study of ocean currents. They began with simulations of ocean currents and hypothesized the existence of unstable manifolds in the ocean around Antarctica. Based on this idea, they then looked for evidence of these unstable manifolds in satellite data, and found then. Finally, to VERIFY their existence, then went to Antarctica to drop floats in the ocean to track where they go. These floats followed the expected manifolds! 

That’s taking simulation informatics — the initial simulation studies — all the way to real world science!

See the outline here: http://meetings.siam.org/sess/dsp_programsess.cfm?SESSIONCODE=14996 

I’m looking for relevant papers to post, but don’t expect to get around to it soon, so don’t expect them to make their way onto this page.


Simulation Informatics @ SIAMUQ

We’ve had great audience participation for our minisymposium at SIAM UQ 2012 so far!

There were 20-30 people in the audience throughout the entire mini-symposium.

Here’s a log of what’s going on!

5:30pm We had to cutoff questions for Barnabas’s talk! Please discuss here if you are interested.

5:35pm Qiqi Wang is now talking about unconventional information in simulations

5:37pm Qiqi on data and simulation: “This is the way we envision the future is going to be”

5:40pm Still Qiqi … “When you go to a chaotic dynamical system, [the adjoint method] breaks down.”  “Let me tell you the solution we just worked out.”

5:42pm When you use time-averaging, you get a smaller magnitude of noise, but that makes the derivative of the noise larger (10^100)!.  The difference between the finite time average and the infinite time average suffers from the butterfly effect, and this is a huge problem.

5:45pm Qiqi’s talk is too interesting, I need to pay attention now.  The video will be online soon 🙂

5:55pm Qiqi is solving challenges on how to get information out of unsteady simulations.

6:00pm Chandrika Kamath is starting to talk about scientific data mining.

6:03pm Kamath – “80-90% of the time is spent in data processing” [in scientific data mining]

6:07pm Kamath – classifying Poincare orbits is a tough challenge due to multi-scale and fractal patterns in the data, not to mention noise

6:09pm Kamath – feature building for the Poincare orbits include using polar coordinates to exaggerate some features and using residuals in polynomial fits

6:13pm Kamath – using parallel plots help to identify useful regions in a object by feature matrix

6:15pm Kamath – using 5-6 features incredibly good performance

6:15pm Kamath – Rayleigh-Taylor instabilities, 30TB and 80TB of data.  These are big problems!

6:17pm The problem – finding bubbles and spikes in tubulence data – isn’t even well defined.  They used image analysis techniques.

6:22pm Darn, we lost the video (out of battery) for the end of Chandrika’s talk.