  The feasibility of uniquely estimating parameters of dynamical systems from
observations is a widely discussed aspect of mathematical modelling. Several
approaches have been published for analyzing identifiability. However, they are
typically computationally demanding, difficult to perform and/or not applicable
in many application settings.
  Here, an intuitive approach is presented which enables quickly testing of
parameter identifiability. Numerical optimization with a penalty in radial
direction enforcing displacement of the parameters is used to check whether
estimated parameters are unique, or whether the parameters can be altered
without loss of agreement with the data indicating non-identifiability. This
Identifiability-Test by Radial Penalization (ITRP) can be employed for every
model where optimization-based fitting like least-squares or maximum likelihood
is feasible and is therefore applicable for all typical deterministic models.
  The approach is illustrated and tested using 11 ordinary differential
equation (ODE) models. The presented approach can be implemented without great
efforts in any modelling framework. It is available within the free
Matlab-based modelling toolbox Data2Dynamics. Source code is available at
https://github.com/Data2Dynamics.
