Determination of statistical steady states of thermal convection aided by machine learning predictions

Kurzfassung

imulating thermal convection in Earth and planetary interiors is crucial for various applications, including benchmarking numerical codes, deriving scaling laws for heat transfer in complex flows, determining mixing efficiency and the characteristic spatial wavelengths of convection. However, reaching a statistically-steady state in these simulations, even in 2D, can be computationally expensive. While choosing "close-enough" initial conditions can significantly speed simulations up, this selection process can be challenging, especially for systems with multiple controlling parameters. This work explores how machine learning can be leveraged to identify optimal initial conditions, ultimately accelerating numerical convection simulations on their path to a statistically-steady state.