Title: Characterizing Nonlinear Dynamics via Smooth Prototype Equivalences
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: normalizing flows, single cell, scRNA-seq

Abstract: Characterizing dynamical systems given limited measurements is a common challenge throughout the physical and biological sciences. However, this task is challenging, especially due to transient variability in systems with equivalent long-term dynamics. We address this by introducing smooth prototype equivalences (SPE), a framework matches between sparse observations of phase space and prototypical behaviors using invertible neural networks. SPE enables classification by comparing the deformation loss of the observed sparse measurements to the prototype dynamics. Furthermore, our approach enables estimation of the invariant sets of the observed dynamics through the learned mapping from prototype space to data space. Our method outperforms existing techniques in the classification of oscillatory systems and can efficiently identify invariant structures like limit cycles and fixed points in an equation-free manner, even when only a small, noisy subset of the phase space is observed. Finally, we show how our method can be used for the detection of biological processes like the cell cycle trajectory from high-dimensional single-cell gene expression data.