Results 41 to 50 of about 2,360 (218)

New Numerical Method for Solving Tenth Order Boundary Value Problems

open access: yesMathematics, 2018
In this paper, we implement reproducing kernel Hilbert space method to tenth order boundary value problems. These problems are important for mathematicians. Different techniques were applied to get approximate solutions of such problems.
Ali Akgül   +3 more
doaj   +1 more source

Reproducing kernel functions for linear tenth-order boundary value problems

open access: yesITM Web of Conferences, 2018
Higher order differential equations have always been an onerous problem to investigate for the mathematicians and engineers. Different numerical methods were applied to get numerical approximations of such problems.
Akgül Ali   +3 more
doaj   +1 more source

Numerical solvability of generalized Bagley–Torvik fractional models under Caputo–Fabrizio derivative

open access: yesAdvances in Difference Equations, 2021
This paper deals with the generalized Bagley–Torvik equation based on the concept of the Caputo–Fabrizio fractional derivative using a modified reproducing kernel Hilbert space treatment.
Shatha Hasan   +5 more
doaj   +1 more source

Analytic Kramer kernels, Lagrange-type interpolation series and de Branges spaces

open access: yes, 2011
The classical Kramer sampling theorem provides a method for obtaining orthogonal sampling formulas. In particular, when the involved kernel is analytic in the sampling parameter it can be stated in an abstract setting of reproducing kernel Hilbert spaces
Hernández-Medina, Miguel A.   +7 more
core   +1 more source

New Berezin symbol inequalities for operators on the reproducing kernel Hilbert space

open access: yes, 2021
We use Kittaneh and Manasrah inequality and Kian’s functional calculus method to prove some new inequalities for Berezin symbols and Berezin numbers of operators.
Tapdıgoğlu, Ramiz, Ramiz Tapdigoglu
core   +1 more source

A new kernel method for the uniform approximation in reproducing kernel Hilbert spaces

open access: yesApplied Mathematics Letters
This paper discussed the uniform approximation of functions on reproducing kernel Hilbert spaces (RKHS). In this direction, classical approximation methods are investigated by Fourier orthogonal projections (assuming that the Fourier coefficients are given) and their discrete versions (assuming that function values are well-distributed).
Themistoclakis, Woula, Van Barel, Marc
openaire   +2 more sources

ORBIT‐AMD: Ordinal Risk, Bilateral Imaging, and Trajectory Learning for Age‐Related Macular Degeneration in Multi‐Cohorts

open access: yesAdvanced Science, EarlyView.
Eligibility flow and real‐world AMD burden in the UKB retinal imaging cohort and TMUEH external‐validation cohort. Overview of the ORBIT‐AMD architecture, integrating retinal representation pretraining, bilateral eye‐graph modeling and concept bottleneck learning to support ordered risk, bilateral context, interpretable lesion concepts, longitudinal ...
Xuehao Cui   +3 more
wiley   +1 more source

Solutions of fractional differential equations with reproducing kernel hilbert space method

open access: yes, 2022
Bu tez yedi bölümden oluşmaktadır. Birinci bölümde, kesir mertebeli türev ve doğuran çekirdekli Hilbert uzayı metodu hakkında tarihsel gelişim ve literatür taraması verilmiştir. İkinci bölümde, tez çalışmasında kullanılacak olan temel tanım, teorem ve ön
Şenol, Mehmet, Ata, Ayşe
core  

Numerical Algorithm for the Third-Order Partial Differential Equation with Three-Point Boundary Value Problem

open access: yesAbstract and Applied Analysis, 2014
A numerical method based on the reproducing kernel theorem is presented for the numerical solution of a three-point boundary value problem with an integral condition.
Jing Niu, Ping Li
doaj   +1 more source

DeepMapper: Attention‐Based AutoEncoder for System Identification in Wound Healing and Stage Prediction

open access: yesAdvanced Intelligent Discovery, EarlyView.
The authors develop a deep learning model for real‐time tracking of wound progression. The deep learning framework maps the nonlinear evolution of a time series of images to a latent space, where they learn a linear representation of the dynamics. The linear model is interpretable and suitable for applications in feedback control.
Fan Lu   +11 more
wiley   +1 more source

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