A central challenge in data-driven model discovery is the presence of hidden, or latent,
variables that are not directly measured but are dynamically important. Takens' theorem …

Sparse system identification is the data-driven process of obtaining parsimonious differential
equations that describe the evolution of a dynamical system, balancing model complexity …

A central challenge in data-driven model discovery is the presence of hidden, or latent,
variables that are not directly measured but are dynamically important. Takens' theorem …

Data-driven discovery of partial differential equations (PDEs) has attracted increasing
attention in recent years. Although significant progress has been made, certain unresolved …

Many natural systems exhibit chaotic behavior, including the weather, hydrology,
neuroscience, and population dynamics. Although many chaotic systems can be described …

The sparse identification of nonlinear dynamics (SINDy) has been established as an
effective technique to produce interpretable models of dynamical systems from time …

Despite rapid progress in data acquisition techniques, many complex physical, chemical,
and biological systems remain only partially observable, thus posing the challenge to …

Emulation plays an important role in engineering design. However, most emulators such as
Gaussian processes (GPs) are exclusively developed for interpolation/regression and their …

Discovering the governing equations of evolving systems from available observations is
essential and challenging. In this paper, we consider a different scenario: discovering …

Ordinary differential equations (ODEs) are a widely used formalism for the mathematical
modeling of dynamical systems, a task omnipresent in scientific domains. The paper …