Latest RMM Blog Entry.

Optimal according to what ? is the latest entry in the Robust Mathematical Modeling blog. Check it out !

RMM: The Modeler's Known Unknowns and Unknown Knowns

I just wrote a new entry on the mathematical modeler's known unknowns and unknown knowns. Check it out!

Robust Mathematical Modeling Example #1: Ground Motion Models for Probabilistic Seismic Hazard Analysis

 

 

I initially wanted to convey the difficulty of modeling physical systems by featuring some examples I knew something about early on the Robust Mathematical Modeling blog. Instead, a reader of the blog, Nicolas Kuehn kindly provided an example he is working on. Where is this discussion going ? I am not sure but I don't see discussions like these often on the web. Many disciplines in engineering and science are facing the same type of problems, a mild case of curse of dimensionality with nonlinear models and a dearth of expensive data.  There are currently 95 people reading the blog through an RSS feed and there is an accompanying LinkedIn group. The more I think about it, the more I think some of the machine learning techniques such as manifold learning ought to be investigated alongside the generic model building capabilities generally deployed with traditional Bayesian statistics. These methods should eventually be part of the bag of tools engineers and scientists in the trenches, use. I mean it's great to do classification on images, video and text but what about doing this for real data and making sense of them ? Do you want to be part of that discussion ? I thought so, invite your friends, they're welcome too...

When Modeling Reality Is Not An Option (The Robust Mathematical Modeling Blog)

Some of you know this but others may not. I sometimes blog about robust mathematical modeling (when modeling reality is not an option is the motto). The reason I say this is because I just wrote an entry there about a workshop I attended last week on issues related to modeling and its pitfalls. One of the issue discussed (the MOX talk) used to be an issue we struggled with in the Excess-Weapons Plutonium disposition program back in the late nineties.  The entry  SCM Talks: Electricity Production Management and the MOX Computational Chain features the fascinating problematic of planning electricity production for France and the computational difficulties stemming from performing experiments and computational calibration exercises outside the real region of operation of a nuclear reactor.  

A new entry in the RMM blog and a follow-up to the Taste of Others, AREVA

I just wrote a small entry entitled High Throughput Testing and TCS meets EDA in the RMM blog. While Nuit Blanche puts a certain emphasis on Compressive Sensing, I sometimes feel it inappropriate to mention other technical issues that may not connect in an obvious fashion to that subject even though I do this occasionally :-)

While we are on the subject of weight reduction through NOse Clipping While Eating (let's call it the NO-C-WE method), I have now lost 28.6 pounds (13 kilograms) ever since I started self experimenting with a nose clip on while eating. I have also done a sub-experiment in the past two months where I have not used the nose clip. While I could keep the same weight, over time it became increasingly clear that the snacking (grignotage in French) would kick start back again. This is fascinating. I am waiting to hear from some friends on how it works out for them. This is so deceptively simple that I feel uncertain as to why people are defensive when told about it.

Finally, every so often I respond to a job announcement because it could fit into some of the things I want to do. This one was an announcement by Areva for the position of Vice President Marketing Reactors. I did not get it but I very much liked the fact that they sent an e-mail out that said I did not get it. Far too often companies do not go to the extra length of answering or simply setting up their servers to send back a negative feedback. Kudos to them.