Patrick Dewilde, TU Delft (The Netherlands), TU Munich (Germany)
Jan Zarzycki, Wroclaw University of Technology (Poland)
Stochastic Modeling is the art of producing an adequate reduced model for a dynamical system driven by one or more stochastic processes.
It is an essential step not only in understanding the behavior of such a system, but also in producing an essential component for `model based control’,
whereby the control law is derived from a good estimate of the system’ structure. In the case of linear (time variant or time invariant)
systems driven by a zero-mean Gaussian process, the problem of stochastic modeling has found some very elegant solutions,
depending on the kind of data available for the identification. The data could e.g., consist of correlations or partial parametrized models.
The problem becomes much more challenging in the case of non-linear systems driven by stochastic processes of general type,
and one may doubt that there would ever be a generally applicable methodology. However, there are interesting classes of systems
for which one can derive and identify adequate (reduced) models.
The session aims at reporting recent activities and new insights in the whole area of stochastic modeling in a non-conventional context.
- Prof. Patrick Dewilde (TU Delft The Netherlands, TU Munich Germany)
- Prof. Sankar Basu (National Science Foundation, Washington DC. USA)
- Prof. John Baras (Univ. of Maryland USA, TU Munich, KTH Stockholm)
- Prof. Karl Johansson (Royal Institute of Technology Stockholm, Sweden)