Welcome the homepage of Gianfranco Durin
Avalanche spatial structure and multivariable scaling functions: sizes, heights, widths, and views through windows
Phys. Rev. E 84, 061103 (2011) [22 pages]
Y-J Chen, S. Papanikolaou, J. P. Sethna, S. Zapperi & G. Durin
We introduce a systematic method for extracting multivariable universal scaling functions and critical exponents from data. We fully characterize the spatial structure of these avalanches—we report universal scaling functions for size, height, and width distributions, and also local front heights. Furthermore, we resolve a problem that arises in many imaging experiments of crackling noise and avalanche dynamics, where the observed distributions are strongly distorted by a limited field of view. Through artificially windowed data, we show these distributions and their multivariable scaling functions may be written in terms of two control parameters: the window size and the characteristic length scale of the dynamics. For the entire system and the windowed distributions we develop accurate parametrizations for the universal scaling functions, including corrections to scaling and systematic error bars, facilitated by a novel software environment SloppyScaling.
Universality beyond power laws and the average avalanche shape
Nat. Phys. 7, 316-320 (2011)
S. Papanikolaou, F. Bohn, R. L. Sommer, G. Durin, S. Zapperi & J. P. Sethna
Power-law scaling of critical phenomena has been most powerful for predictions near a critical point. By averaging the noise emitted by avalanches of a given duration, however, universal scaling functions can extend the predictive power of scaling far from the critical point.
I work on magnetization process of magnetic materials, and on complexity in material science. In particular:
- Barkhausen noise, in bulk and thin films
- Domain wall dynamics in nanostructures with disorder
Find the list of my publications here
Who I am
I also am a big fan of the Python Language that I use everyday to analyse tons of data. Don't you know it? Hmmm, bad, bad... too bad.