While the recent presentations at SCM were enlightening with regards to known problems that are hard to model, I wonder if any of the readers have a specific knowledge in a certain subject area where modeling is difficult. Please contact me and we can probably run a Q&A on this blog. If you want to remain anonymous, because you are feeling uncertain about discussing the uncertainties of the modeling in your area, I can also anonymize the Q&A.

The number of readers of this blog is currently at about 80 but I expect it to grow as this issue of robust modeling keeps raising its ugly head in many different field of science and engineering. Let us recall the areas where robust mathematical modeling might be beneficial:

- The data are missing or corrupted ;
- The laws describing the phenomena are not completely known ;
- The objectives are multiple and contradictory.
- The computational chain has too many variables.

I just added the last one in view of the very interesting presentation by Giovanni Bruna on the problematic of figuring out how to extract meaningful information out of a set of experiments and computations in the case of plutonium use in nuclear reactors.

In the meantime, I'll feature some of the problematic I have seen that never had an easy modeling answer.

P.S:A bleg is a beg on a blog :-)

Credit photo: ESA, NASA

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