  Identification of nonlinear systems is a challenging problem. Physical
knowledge of the system can be used in the identification process to
significantly improve the predictive performance by restricting the space of
possible mappings from the input to the output. Typically, the physical models
contain unknown parameters that must be learned from data. Classical methods
often restrict the possible models or have to resort to approximations of the
model that introduce biases. Sequential Monte Carlo methods enable learning
without introducing any bias for a more general class of models. In addition,
they can also be used to approximate a posterior distribution of the model
parameters in a Bayesian setting. This article provides a general introduction
to sequential Monte Carlo and shows how it naturally fits in system
identification by giving examples of specific algorithms. The methods are
illustrated on two systems: a system with two cascaded water tanks with
possible overflow in both tanks and a compartmental model for the spreading of
a disease.
