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This study concerns the efficiency of time-spectral methods for numerical solution of differential equations. It is found that the time-spectral method GWRM demonstrates insensitivity to stiffness and chaoticity due to the implicit nature…

Numerical Analysis · Mathematics 2024-06-05 Jan Scheffel

In this paper we consider Sparse Fourier Transform (SFT) algorithms for approximately computing the best $s$-term approximation of the Discrete Fourier Transform (DFT) $\mathbf{\hat{f}} \in \mathbb{C}^N$ of any given input vector…

Numerical Analysis · Mathematics 2017-06-12 Sami Merhi , Ruochuan Zhang , Mark A. Iwen , Andrew Christlieb

We present a new version of the fast Gauss transform (FGT) for discrete and continuous sources. Classical Hermite expansions are avoided entirely, making use only of the plane-wave representation of the Gaussian kernel and a new…

Numerical Analysis · Mathematics 2023-05-15 Leslie Greengard , Shidong Jiang , Manas Rachh , Jun Wang

We propose an adaptive approach in picking the wave-number parameter of absorbing boundary conditions for Schr\"{o}dinger-type equations. Based on the Gabor transform which captures local frequency information in the vicinity of artificial…

Numerical Analysis · Mathematics 2009-11-11 Zhenli Xu , Houde Han , Xiaonan Wu

Graphene plasmonic resonators have been broadly studied in the terahertz and mid-infrared ranges because of their electrical tunability and large confinement factors which can enable dramatic enhancement of light-matter coupling. In this…

Recovering a sparse signal from its low-pass projections in the Fourier domain is a problem of broad interest in science and engineering and is commonly referred to as super-resolution. In many cases, however, Fourier domain may not be the…

Information Theory · Computer Science 2019-02-20 Ayush Bhandari , Yonina C. Eldar

Random Feature Models (RFMs) have become a powerful tool for approximating multivariate functions and solving partial differential equations efficiently. Sparse Random Feature Expansions (SRFE) improve traditional RFMs by incorporating…

Numerical Analysis · Mathematics 2026-03-20 Ben Adcock , Khiem Can , Xuemeng Wang

Spatio-temporal receptive field (STRF) models are frequently used to approximate the computation implemented by a sensory neuron. Typically, such STRFs are assumed to be smooth and sparse. Current state-of-the-art approaches for estimating…

Machine Learning · Computer Science 2021-08-23 Ziwei Huang , Yanli Ran , Jonathan Oesterle , Thomas Euler , Philipp Berens

Coherent wide parameter-space searches for continuous gravitational waves are typically limited in sensitivity by their prohibitive computing cost. Therefore semi-coherent methods (such as StackSlide) can often achieve a better sensitivity.…

General Relativity and Quantum Cosmology · Physics 2015-06-03 Reinhard Prix , Miroslav Shaltev

Pulsar Timing Array (PTA) searches for nHz gravitational-wave backgrounds (GWBs) typically model time-correlated noise by assuming a diagonal covariance in Fourier space, neglecting inter-frequency correlations introduced by the finite…

Instrumentation and Methods for Astrophysics · Physics 2025-06-18 Marco Crisostomi , Rutger van Haasteren , Patrick M. Meyers , Michele Vallisneri

Stochastic dynamics on sparse graphs and disordered systems often lead to complex behaviors characterized by heterogeneity in time and spatial scales, slow relaxation, localization, and aging phenomena. The mathematical tools and…

Disordered Systems and Neural Networks · Physics 2025-06-05 Mattia Tarabolo , Luca Dall'Asta

Neural forecasting of spatiotemporal time series drives both research and industrial innovation in several relevant application domains. Graph neural networks (GNNs) are often the core component of the forecasting architecture. However, in…

Machine Learning · Computer Science 2023-02-21 Andrea Cini , Ivan Marisca , Filippo Maria Bianchi , Cesare Alippi

A wideband Gaussian Noise Model of the nonlinear noise power spectral density is developed for a single semiconductor optical amplifier as described by the Agrawal model. A simple, interpretable closed-form expression is obtained for the…

Optics · Physics 2026-03-16 Hartmut Hafermann

Fractional Burgers equations pose substantial challenges for classical numerical methods due to the combined effects of nonlocality and shock-forming nonlinear dynamics. In particular, linear approximation frameworks-such as spectral,…

Numerical Analysis · Mathematics 2026-01-09 Haojun Qin , Zhiwei Gao , Jinye Shen , George Karniadakis

Gabor frames have gained considerable popularity during the past decade, primarily due to their substantiated applications in diverse and widespread fields of engineering and science. Finding general and verifiable conditions which imply…

Functional Analysis · Mathematics 2016-10-31 Firdous A. Shah

Let $(g_{nm})_{n,m\in Z}$ be a Gabor frame for $L_2(R)$ for given window $g$. We show that the window $h^0=S^{-1/2} g$ that generates the canonically associated tight Gabor frame minimizes $\|g-h\|$ among all windows $h$ generating a…

Functional Analysis · Mathematics 2025-10-20 A. J. E. M Janssen , Thomas Strohmer

We propose the frozen Gaussian approximation for computation of high frequency wave propagation. This method approximates the solution to the wave equation by an integral representation. It provides a highly efficient computational tool…

Numerical Analysis · Mathematics 2011-03-16 Jianfeng Lu , Xu Yang

The topic of this paper are (multi-window) Gabor frames for signals over finite Abelian groups, generated by an arbitrary lattice within the finite time-frequency plane. Our generic approach covers simultaneously multi-dimensional signals…

Group Theory · Mathematics 2008-03-17 H. G. Feichtinger , W. Kozek , F. Luef

Sum-frequency generation (SFG) spectroscopy provides a versatile method for the investigation of non-centrosymmetric media and interfaces. Here, using tunable picosecond infrared (IR) pulses from a free-electron laser, the nonlinear optical…

Materials Science · Physics 2023-08-01 Riko Kiessling , Martin Wolf , Alexander Paarmann

Spatial-temporal graph models are prevailing for abstracting and modelling spatial and temporal dependencies. In this work, we ask the following question: whether and to what extent can we localise spatial-temporal graph models? We limit…

Machine Learning · Computer Science 2023-06-16 Wenying Duan , Xiaoxi He , Zimu Zhou , Lothar Thiele , Hong Rao