Discovering governing equations from data by sparse identification of nonlinear dynamical systems.

ABSTRACT: Extracting governing equations from data is a central challenge in many diverse areas of science and engineering. Data are abundant whereas models often remain elusive, as in climate science, neuroscience, ecology, finance, and epidemiology, to name only a few examples. In this work, we combine sparsity-promoting techniques and machine learning with nonlinear dynamical systems to discover governing equations from noisy measurement data. The only assumption about the structure of the model is that there are only a few important terms that govern the dynamics, so that the equations are sparse in the space of possible functions; this assumption holds for many physical systems in an appropriate basis. In particular, we use sparse regression to determine the fewest terms in the dynamic governin

SUBMITTER: Brunton SL 

PROVIDER: S-EPMC4839439 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature