Abstract Physics-Informed Neural Networks (PINN) are neural networks (NNs) that encode
model equations, like Partial Differential Equations (PDE), as a component of the neural …

We propose a fast sequential algorithm for the fundamental problem of estimating
frequencies and amplitudes of a noisy mixture of sinusoids. The algorithm is a natural …

Extremely diverse service requirements are one of the critical challenges for the upcoming
fifth-generation (5G) radio access technologies. As a solution, mixed numerologies …

In this paper, the Newton-conjugate-gradient methods are developed for solitary wave
computations. These methods are based on Newton iterations, coupled with conjugate …

We consider a class of popular distributed non-convex optimization problems, in which
agents connected by a network ς collectively optimize a sum of smooth (possibly non …

Gaussian mixture models (GMMs) are a family of generative models used extensively in
many machine learning applications. The modeling power of GMMs is directly linked to the …

Different energy systems become highly connected to provide better flexibility. However, this
change poses new challenges for system management considering the diversity of …

A well-known problem in computing some matrix functions iteratively is the lack of a clear,
commonly accepted residual notion. An important matrix function for which this is the case is …

Due to their tailorable optical properties, organic semiconductors show considerable
promise for use in indoor photovoltaics (IPVs), which present a sustainable route for …

Abstract Near Rectilinear Halo Orbits (NRHOs) are stable or nearly stable orbits that are
defined as part of the L1 and L2 halo orbit families in the circular restricted three-body …