Bayesian uncertainty quantification for data-driven equation learning.

ABSTRACT: Equation learning aims to infer differential equation models from data. While a number of studies have shown that differential equation models can be successfully identified when the data are sufficiently detailed and corrupted with relatively small amounts of noise, the relationship between observation noise and uncertainty in the learned differential equation models remains unexplored. We demonstrate that for noisy datasets there exists great variation in both the structure of the learned differential equation models and their parameter values. We explore how to exploit multiple datasets to quantify uncertainty in the learned models, and at the same time draw mechanistic conclusions about the target differential equations. We showcase our results using simulation data from a relatively s

SUBMITTER: Martina-Perez S 

PROVIDER: S-EPMC8548080 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature