Identifying dynamical systems with bifurcations from noisy partial   observation
  Dynamical systems are used to model a variety of phenomena in which the bifurcation structure is a fundamental characteristic. Here we propose a statistical machine-learning approach to derive lowdimensional models that automatically integrate information in noisy time-series data from partial observations. The method is tested using artificial data generated from two cell-cycle control system models that exhibit different bifurcations, and the learned systems are shown to robustly inherit the bifurcation structure. 
Yohei Kondo, Kunihiko Kaneko, and Shuji Ishihara
q-bio.QM