into cores ([@navarro_cores_1996], [@read_mass_2005], and [@mashchenko_stellar_2008]). In the latter case we wish better to understand the evolution of dSphs and as such require robust dynamical modelling of their end-states to act as targets for evolutionary simulations.

To date, dynamical modelling of dSphs has for the most part been based on very restrictive simplifying assumptions, namely that the dSphs are spherical and in equilibrium (see, for example, [@wilkinson_dark_2002], [@walker_universal_2009], [@strigari_kinematics_2010], and [@read_how_2017]). While some authors have considered more general models [@breddels_model_2013], relaxation of these assumptions results in models that are significantly more computationally expensive, often prohibitively so. However, it is possible to reduce this computational expense by borrowing the technique of *emulation* from machine learning.

Let us establish some terminology. A *simulation* is a model of some phenomenon. Formally, a model is an indexed set of functions $\{f(\cdot\,;a)\}_{a \in A}$ where $f(\cdot\,;a): X \longrightarrow Y$ is a function, $a$ is called a *parameter* and the set $A$ is called the *parameter space*. Specifically, in this paper, we assume some model of the phase-space distribution function, $f(\cdot\,;a)$, of the stars within a dSph, where $a$ parameterizes the physical model we are using. In this case, $X$ is the phase space and $Y$ 