Stability selection enables robust learning of differential equations from limited noisy data.

ABSTRACT: We present a statistical learning framework for robust identification of differential equations from noisy spatio-temporal data. We address two issues that have so far limited the application of such methods, namely their robustness against noise and the need for manual parameter tuning, by proposing stability-based model selection to determine the level of regularization required for reproducible inference. This avoids manual parameter tuning and improves robustness against noise in the data. Our stability selection approach, termed PDE-STRIDE, can be combined with any sparsity-promoting regression method and provides an interpretable criterion for model component importance. We show that the particular combination of stability selection with the iterative hard-thresholding algorithm from

SUBMITTER: Maddu S 

PROVIDER: S-EPMC9199075 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature