Stochastic Dynamics and Data Science

November 23, 9:00-11:00 Am, Central Time 


Nonlinearity and stochasticity are extremely important factors in a wide variety of areas, including civil engineering, mechanical engineering, aerospace engineering, and so on. The systems with nonlinearity and stochasticity could provide an accurate mathematical/physical framework to describe the real world in comparison with the deterministic systems.

With the development of data and computational science, the interactions between data science and random dynamical systems are becoming exciting and have aroused many scholars’ widespread interest. Data-driven techniques as a powerful tool in many real-world applications are indispensable for understanding the dynamic behaviors of real systems from noisy observation data. This mini-symposia is organized to collect and discuss recent advance in application of data science in the stochastic dynamics in order to promote the rapid development of related fields. Relevant contributions in the areas of stochastic dynamics and data science are welcome. Topics of interests in Stochastic Dynamics and Data Science include but are not limited to the following aspects:

  • Stochasticity quantification and analysis
  • Data-driven modeling and analysis of nonlinear stochastic systems
  • Data-driven understanding, prediction and control of nonlinear stochastic systems
  • Data-driven reliability analysis of nonlinear stochastic systems
  • Inverse problems of nonlinear stochastic systems
  • Machine learning meets nonlinear stochastic systems

Professor Yong Xu
Northwestern Polytechnical University, Shaanxi, China

Professor Ronghua Huan
Zhejiang University, Zhejiang, China

Professor Shaojuan Ma
Beifang Mingzu University, Ning Xia, China

Professor Yanfei Jin
Beijing Institute of Technology, Beijing, China