Data-Driven Discovery of mechanical models directly from k-space data: initial results with an actively forced linear elastic system.

We propose a framework for data-driven discovery of the governing biomechanical equations for soft tissue directly from k-space data. This framework is based on principles from Spectro-dynamics, a novel method to infer dynamical information directly from severely undersampled spectral data with high spatiotemporal resolution, and sparse regression. As a proof-of-concept, the underlying equations of motion are identified for an actively forced linear elastic system using experimentally obtained k-space data. The systems studied in this abstract are successfully identified with errors of less than 2.5% on the system parameters, and their respective displacement fields are reconstructed accurately.