First-order fluidification of stochastic (discrete) PN models leads to deterministic and continuous models. Nevertheless, basic qualitative properties, like absence of deadlocks are not preseved in general (e.g. you may have a live and bounded discrete time-stochastic model, while the corresponding continuous model is unbounded and non-live!). Therefore results concerning continuisable net subclasses are required.
Another interesting reflection concerning continuous net models is that, even if the continuous approximation (or view) is stable, it may happen that no steady-state exists (a clearly non-linear behaviour for the continuous relaxed model). This is another "unexpected" property, in the sense that works in the field of continuous models usually assume its existence.
The presentation will also introduce some design problems, like the computation of a minimal initial marking, that surprisingly happens to be polynomial for certain net subclasses. Some control results will be introduced, making a bridge to scheduling in the underlying discrete model.