Results 81 to 90 of about 13,358 (271)

On the weak limit of compact operators on the reproducing kernel Hilbert space and related questions

open access: yesAnalele Stiintifice ale Universitatii Ovidius Constanta: Seria Matematica, 2016
By applying the so-called Berezin symbols method we prove a Gohberg- Krein type theorem on the weak limit of compact operators on the non- standard reproducing kernel Hilbert space which essentially improves the similar results of Karaev [5]: We also in ...
Saltan Suna
doaj   +1 more source

Solutions to Uncertain Volterra Integral Equations by Fitted Reproducing Kernel Hilbert Space Method

open access: yes, 2016
We present an efficient modern strategy for solving some well-known classes of uncertain integral equations arising in engineering and physics fields. The solution methodology is based on generating an orthogonal basis upon the obtained kernel function ...
G. Gumah   +3 more
semanticscholar   +1 more source

Effectiveness of low‐density high‐throughput marker platform and easy‐to‐measure traits for genomic prediction of biomass yield in oat (Avena sativa L.)

open access: yesThe Plant Genome, Volume 19, Issue 1, March 2026.
Abstract Genomic selection (GS) is a promising strategy for accelerating genetic gains of complex traits in breeding programs. Despite the recent advancements in high‐throughput genotyping technologies, the selection of the type of marker systems needed for GS remains challenging in breeding programs.
Samuel A. Adewale   +14 more
wiley   +1 more source

TVB C++: A Fast and Flexible Back‐End for The Virtual Brain

open access: yesAdvanced Science, Volume 13, Issue 2, 9 January 2026.
TVB C++ is a streamlined and fast C++ Back‐End for The Virtual Brain (TVB), designed to make it as flexible as TVB, and FAST. Another pillar is to be fully compatible with TVB so easy bindings can be created from Python. Users can easily configure TVB C++ to execute the same code but with enhanced performance and parallelism.
Ignacio Martín   +7 more
wiley   +1 more source

Infinite-Dimensional Stochastic Transforms and Reproducing Kernel Hilbert space [PDF]

open access: green, 2022
Palle E. T. Jørgensen   +2 more
openalex   +1 more source

Solving nonlocal initial‐boundary value problems for parabolic and hyperbolic integro‐differential equations in reproducing kernel hilbert space

open access: yes, 2017
This article is concerned with a method for solving nonlocal initial‐boundary value problems for parabolic and hyperbolic integro‐differential equations in reproducing kernel Hilbert space.
M. Fardi, M. Ghasemi
semanticscholar   +1 more source

Quantifying the Single‐Cell Morphological Landscape of Cellular Transdifferentiation through Force Field Reconstruction

open access: yesAdvanced Science, Volume 13, Issue 1, 5 January 2026.
This study reconstructs the driving force field of fibroblast‐to‐neuron transdifferentiation from sparse single‐cell images by decomposing it into flux and time‐dependent potential gradient, extending the landscape‐flux framework to non‐steady‐state systems.
Chudan Yu   +3 more
wiley   +1 more source

A representation for a weighted L2 space [PDF]

open access: yesSurveys in Mathematics and its Applications, 2017
Using elementary tools of complex analysis and Hilbert space theory, we present a realization of a weighted L2 space on the unit disc. In the way, we show some additional properties.
Martha Guzman-Partida   +1 more
doaj  

Regularization in Reproducing Kernel Hilbert Spaces

open access: yes, 2022
AbstractMethods for obtaining a functiongin a relationship$$y=g(x)$$y=g(x)from observed samples ofyandxare the building blocks for black-box estimation. The classical parametric approach discussed in the previous chapters uses a function model that depends on a finite-dimensional vector, like, e.g., a polynomial model.
Gianluigi Pillonetto   +4 more
openaire   +1 more source

Machine Learning for Accelerating Energy Materials Discovery: Bridging Quantum Accuracy with Computational Efficiency

open access: yesAdvanced Energy Materials, Volume 16, Issue 2, 14 January 2026.
This perspective highlights how machine learning accelerates sustainable energy materials discovery by integrating quantum‐accurate interatomic potentials with property prediction frameworks. The evolution from statistical methods to physics‐informed neural networks is examined, showcasing applications across batteries, catalysts, and photovoltaics ...
Kwang S. Kim
wiley   +1 more source

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