High-fidelity blood flow modelling is crucial for enhancing our understanding of
cardiovascular disease. Despite significant advances in computational and experimental …

Abstract Sparse Identification of Nonlinear Dynamics (SINDy) is a method of system
discovery that has been shown to successfully recover governing dynamical systems from …

We present a novel weak formulation and discretization for discovering governing equations
from noisy measurement data. This method of learning differential equations from data fits …

A novel structural health monitoring approach is developed by coupling the inverse finite
element method (iFEM) and peridynamic theory (PD) for real-time shape sensing analysis …

Plasmas are highly nonlinear and multiscale, motivating a hierarchy of models to
understand and describe their behavior. However, there is a scarcity of plasma models of …

Discovering nonlinear differential equations that describe system dynamics from empirical
data is a fundamental challenge in contemporary science. While current methods can …

Recent advances in the field of data-driven dynamics allow for the discovery of ODE systems
using state measurements. One approach, known as Sparse Identification of Nonlinear …

Sparse identification of nonlinear dynamics (SINDy) is a recent nonlinear modeling
technique that has demonstrated superior performance in modeling complex time-series …

Chaotic analog circuits are commonly used to demonstrate the physical existence of chaotic
systems and investigate the variety of possible applications. A notable disparity between the …

Multistability is an inherent property of many nonlinear dynamical systems. However, finding
exact conditions where a certain nonlinear system is multistable, is a complex problem. In …