SubTSBR to tackle high noise and outliers for data-driven discovery of differential equations

Understand

Data-driven discovery of differential equations has been an emerging research topic.

• We propose a novel algorithm subsampling-based threshold sparse Bayesian regression (SubTSBR) to tackle high noise and outliers.
• The subsampling technique is used for improving the accuracy of the Bayesian learning algorithm.
• It has two parameters: subsampling size and the number of subsamples.