Learning normal form autoencoders for data-driven discovery of universal,parameter-dependent governing equations

1 code implementation • 9 Jun 2021 • Manu Kalia, Steven L. Brunton, Hil G. E. Meijer, Christoph Brune, J. Nathan Kutz

In this work, we introduce deep learning autoencoders to discover coordinate transformations that capture the underlying parametric dependence of a dynamical system in terms of its canonical normal form, allowing for a simple representation of the parametric dependence and bifurcation structure.

Model Discovery