Characterizing Nonlinear Dynamics via Smooth Prototype Equivalences
This addresses a common problem in physical and biological sciences for researchers needing to analyze complex systems with sparse data, though it is incremental as it builds on existing methods like normalizing flows.
The paper tackles the challenge of characterizing dynamical systems from limited, noisy measurements by introducing smooth prototype equivalences (SPE), a framework that uses normalizing flows to map data to simplified prototypes, enabling classification and estimation of invariant sets like limit cycles and fixed points. It outperforms existing techniques in classifying oscillatory systems and demonstrates applicability to biological data such as cell cycle trajectories from single-cell gene expression.
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 that fits a diffeomorphism using normalizing flows to distinct prototypes - simplified dynamical systems that define equivalence classes of behavior. SPE enables classification by comparing the deformation loss of the observed sparse, high-dimensional 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.