Core-collapse supernovae (CCSNe) are the explosive deaths of massive stars. While CCSNe are crucial to many aspects of our understanding of the universe, including the synthesis of the elements, the physical mechanism that drives these explosions is not fully understood. While three-dimensional simulations of core-collapse supernovae are the physically accurate representation of the real phenomenon, they use a notorious amount of computing power. 1D simulations are less demanding but fail to reproduce many of the physical effects of 3D calculations. We explore a new model for including convection and turbulence in 1D simulations that mimics 3D simulations in a realistic manner. Our model requires fitting model parameters to 3D simulation data. Ny including the proper coefficients of turbulent diffusion and convective mixing length in our model, 1D simulations can be executed in a way that reproduces the results of 3D simulations. If successful, this method could potentially save valuable computing time and allow for fast, accurate testing of future hypotheses. In this study, we construct a Gaussian Process Emulator of the parameter space and use Markov Chain Monte Carlo (MCMC) methods to find optimal values for the mixing-length coefficient __ and diffusion coefficient _D. Once __ and _D are found empirically, they will be used to close the model equations governing the turbulent dynamics in 1D simulations of CCSNe during the period immediately following the core bounce.