Abstract

This work is concerned with learning the dynamics of technical systems from data within a sparse Bayesian framework. The approach employs a basis representation of the unknown dynamics function, similar to the sparse identification of nonlinear dynamics (SINDy) approach, which is combined with a Bayesian procedure for parameter estimation. We propose to use the recently introduced neuronized priors as a unified approach to enforce sparsity in a dynamical systems context, and illustrate the method with an academic example.