Studying how rocky planets like Mercury, Venus, the Earth and Mars evolve over billions of years requires detailed modelling of mantle convection, the main driver of planetary evolution. The mantle - sandwiched between the crust and the core - behaves like a highly viscous fluid over geological time scales and hence can be quantified through equations describing conservation of mass, momentum and energy. These non-linear partial differential equations are typically solved numerically using fluid dynamics codes. However, the parameters and initial conditions to these equations are poorly known. Whereas certain outputs of the simulations (numerically solved equations) can be "observed'' via spacecraft missions and used to constrain key parameters and initial conditions, thus elucidating the basic physics and evolution of planets. Since each simulation can take from several hours to weeks to run, varying parameters extensively and repeatedly is often impractical. We aim to overcome this computational bottleneck by learning the mapping between parameters and observables through a combination of state-of-the-art geodynamic modelling, machine learning and high-performance computing.