The Science of Disasters: Climate Disruptions, Heart Attacks, and Market Crashes
Armin Bunde, Jürgen Kropp, Hans-Joachim Schellnhuber
Springer Science & Business Media, Dec 6, 2012 - Science - 453 pages
Are there universal laws governing the persistence of weather, and is it possible to predict climate transitions as generated by natural or man-made perturbations? How can one quantify the roller-coaster dynamics of stock markets and anticipate mega-crashes? Can we diagnose the health condition of patients from heartbeat time-series analysis, which may even form the basis for infarct prevention? This book tackles these questions by applying advanced methods from statistical physics and related fields to all types of non-linear dynamics prone to disaster. The transdisciplinary analysis is organized in some dozen review articles written by world-class scientists.
What people are saying - Write a review
We haven't found any reviews in the usual places.
SpaceTime Variability of the European Climate
Is Climate Predictable?
Fractal and Multifractal Approaches
Physiological Relevance of Scaling
Local Scaling Properties for Diagnostic Purposes
in the Paddlefish Electroreceptor
Crowd Disasters and Simulation
Investigations of Financial Markets
Other editions - View all
A.L. Goldberger analysis analyzing wavelet Argoul Arneodo average behavior cascade Chap characterized circulation climate change complexity computed correlation functions corresponding cyclone datasets defined detrended fluctuation analysis distribution DNA walks dynamics efficient market hypothesis entropy exponent H fluctuations fractal fractal dimension frequency Gaussian genomes H.E. Stanley Havlin heart failure heart rate heartbeat Hölder exponent Hurst exponent indicate Legendre transforming Lett Lévy linear log-normal long-range correlations maxima lines measures monofractal multifractal multifractal analysis mutual information Muzy noise nonlinear North Atlantic North Atlantic Oscillation nucleosomes observed obtained parameter partition function Phys physical power-law prediction properties random regime scale invariance scaling exponent Sect shown in Fig signal simulations singularity spectrum sleep sleep apnea space-scale spectra statistical stochastic storm track structure temperature tion trends turbulent values variance W-cascade wavelet transform WT skeleton WTMM WTMM method WTMMM