  Diverse domains of science and engineering use parameterised mechanistic
models. Engineers and scientists can often hypothesise several rival models to
explain a specific process or phenomenon. Consider a model discrimination
setting where we wish to find the best mechanistic, dynamic model candidate and
the best model parameter estimates. Typically, several rival mechanistic models
can explain the available data, so design of dynamic experiments for model
discrimination helps optimally collect additional data by finding experimental
settings that maximise model prediction divergence. We argue there are two main
approaches in the literature for solving the optimal design problem: (i) the
analytical approach, using linear and Gaussian approximations to find
closed-form expressions for the design objective, and (ii) the data-driven
approach, which often relies on computationally intensive Monte Carlo
techniques. Olofsson et al. (ICML 35, 2018) introduced Gaussian process (GP)
surrogate models to hybridise the analytical and data-driven approaches, which
allowed for computationally efficient design of experiments for discriminating
between black-box models. In this study, we demonstrate that we can extend
existing methods for optimal design of dynamic experiments to incorporate a
wider range of problem uncertainty. We also extend the Olofsson et al. (2018)
method of using GP surrogate models for discriminating between dynamic
black-box models. We evaluate our approach on a well-known case study from
literature, and explore the consequences of using GP surrogates to approximate
gradient-based methods.
