Results 251 to 260 of about 355,626 (313)

Convergence properties of dynamic mode decomposition for analytic interval maps

open access: yesCommunications on Pure and Applied Mathematics, Volume 79, Issue 2, Page 179-206, February 2026.
Abstract Extended dynamic mode decomposition (EDMD) is a data‐driven algorithm for approximating spectral data of the Koopman operator associated to a dynamical system, combining a Galerkin method with N$N$ functions and a quadrature method with M$M$ quadrature nodes.
Elliz Akindji   +3 more
wiley   +1 more source

Dimer models and conformal structures

open access: yesCommunications on Pure and Applied Mathematics, Volume 79, Issue 2, Page 340-446, February 2026.
Abstract Dimer models have been the focus of intense research efforts over the last years. Our paper grew out of an effort to develop new methods to study minimizers or the asymptotic height functions of general dimer models and the geometry of their frozen boundaries.
Kari Astala   +3 more
wiley   +1 more source

Multithread Approximation: An OpenMP Constructor

open access: yesConcurrency and Computation: Practice and Experience, Volume 38, Issue 4, February 2026.
ABSTRACT This study introduces an OpenMP construct designed to simplify and unify the integration of approximate computing techniques into shared‐memory parallel programs. Approximate Computing leverages the inherent error tolerance of many applications to trade computational accuracy for gains in performance and energy efficiency.
João Briganti de Oliveira   +2 more
wiley   +1 more source

Incorporating Scale Uncertainty into Differential Expression Analyses Using ALDEx2

open access: yesCurrent Protocols, Volume 6, Issue 2, February 2026.
Abstract Differential abundance or expression analyses are routinely performed on metagenomic, metatranscriptomic, and amplicon sequencing data. In such datasets, analysts usually have no information regarding the true scale (i.e., size) of the microbial community or sample under study, with inter‐sample differences in sequencing depth instead being ...
Scott J. Dos Santos, Gregory B. Gloor
wiley   +1 more source

Seismic Structural Response and Loss Estimation for Dense Urban Districts Using Neural Network Parameterized Gaussian Process

open access: yesEarthquake Engineering &Structural Dynamics, Volume 55, Issue 2, Page 397-412, February 2026.
ABSTRACT Earthquakes pose a major threat to urban areas, causing fatalities, injuries, and significant economic losses. This study proposes a Gaussian process parametrized by deep neural networks (DNN–GP) as an efficient surrogate for assessing seismic losses of building structures at a regional scale.
Byeongseong Choi   +2 more
wiley   +1 more source

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