Designing Attractive Models via Automated Identification of Chaotic and   Oscillatory Dynamical Regimes
  Chaos and oscillations continue to capture the interest of both the scientific and public domains. Yet despite the importance of these qualitative features, most attempts at constructing mathematical models of such phenomena have taken an indirect, quantitative approach, e.g. by fitting models to a finite number of data-points. Here we develop a qualitative inference framework that allows us to both reverse engineer and design systems exhibiting these and other dynamical behaviours by directly specifying the desired characteristics of the underlying dynamical attractor. This change in perspective from quantitative to qualitative dynamics, provides fundamental and new insights into the properties of dynamical systems. 
Daniel SIlk, Paul D.W. Kirk, Chris P. Barnes, Tina Toni, Anna Rose,   Simon Moon, Margaret J. Dallman, Michael P.H. Stumpf
stat.AP math.DS q-bio.QM