Data-driven Identification of Parametric Governing Equations of Dynamical Systems Using the Signed Cumulative Distribution Transform.

This paper presents a novel data-driven approach to identify partial differential equation (PDE) parameters of a dynamical system. Specifically, we adopt a mathematical "transport" model for the solution of the dynamical system at specific spatial locations that allows us to accurately estimate the model parameters, including those associated with structural damage. This is accomplished by means of a newly-developed mathematical transform, the signed cumulative distribution transform (SCDT), which is shown to convert the general nonlinear parameter estimation problem into a simple linear regression. This approach has the additional practical advantage of requiring no a priori knowledge of the source of the excitation (or, alternatively, the initial conditions). By using training data, we devise a coarse regression procedure to recover different PDE parameters from the PDE solution measured at a single location. Numerical experiments show that the proposed regression procedure is capable of detecting and estimating PDE parameters with superior accuracy compared to a number of recently developed machine learning methods. Furthermore, a damage identification experiment conducted on a publicly available dataset provides strong evidence of the proposed method's effectiveness in structural health monitoring (SHM) applications. The Python implementation of the proposed system identification technique is integrated as a part of the software package PyTransKit [1].

Broadband nonreciprocal linear acoustics through a non-local active metamaterial.

The ability to create linear systems that manifest broadband nonreciprocal wave propagation would provide for exquisite control over acoustic signals for electronic filtering in communication and noise control. Acoustic nonreciprocity has predominately been achieved by approaches that introduce nonlinear interaction, mean-flow biasing, smart skins, and spatio-temporal parametric modulation into the system. Each approach suffers from at least one of the following drawbacks: the introduction of modulation tones, narrow band filtering, and the interruption of mean flow in fluid acoustics. We now show that an acoustic media that is non-local and active provides a new means to break reciprocity in a linear fashion without these deleterious effects. We realize this media using a distributed network of interlaced subwavelength sensor-actuator pairs with unidirectional signal transport. We exploit this new design space to create a stable metamaterial with non-even dispersion relations and electronically tunable nonreciprocal behavior over a broad range of frequencies.