Sparse Identification of Nonlinear Dynamical systems (SINDy)
Discovering the governing equations from scientific data becomes easier using data-driven approaches. Sparse regression enables the tractable identification of both the structure and parameters of a nonlinear dynamical system from data. The resulting models have the fewest terms necessary to describe the dynamics, balancing model complexity with the descriptive ability and thus promoting interpretability and generalizability. In this work, we design a custom autoencoder to discover a coordinate transformation into a reduced space where the dynamics may be sparsely represented.We combine the strength of the autoencoder for coordinate representation in a reduced state of the system and sparse identification of nonlinear dynamics (SINDy) for parsimonious models. We implemented this method on the planar pushing task and compared it against a globally linear, Embed to Control(E2C) latent space model. final_report.pdf