Efficient Bayesian Kernel Learning for Discovering Governing Equations from Sparse and Noisy Data

A novel equation discovery method based on Kernel learning and Bayesian Spike-and-Slab priors (KBASS) that is flexible, expressive, and robust to data sparsity and noise, with efficient posterior inference and function estimation.