While different types ofsparsity havebeen proposed toimproverobustness, the generalization properties ofNODEsfordynamical systemsbeyondtheobserved dataareunderexplored. Wesystematically studytheinfluenceofweight andfeature sparsity on forecasting as well as on identifying the underlying dynamical laws.

A simple and robust implementation algorithm is also provided, which can be used for any pre-trained neural network classifiers.

While our approach holds for arbitrary distributions, we instantiate it with Dirichlet distributions: this allows foraclosed-form anddifferentiable expression fortheexpected risk, which then turns the generalization bound into a tractable training objective.

Bayesian inference allows expressing the uncertainty of posterior belief under a probabilistic model given prior information and the likelihood of the evidence. Predominantly, the likelihood function is only implicitly established by a simulator posing the need for simulation-based inference (SBI).