Results 51 to 60 of about 52,154 (284)
Abstract and Applied Analysis, 2013 The present paper emphasizes Jeffery-Hamel flow: fluid flow between two rigid plane walls, where the angle between them is 2α. A new method called the reproducing kernel Hilbert space method (RKHSM) is briefly introduced.
Mustafa Inc +2 more
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Mathematical Modelling and Analysis, 2013 In this work, we investigate a sequence of approximations converging to the existing unique solution of a multi-point boundary value problem(BVP) given by a linear fourth-order ordinary differential equation with variable coeffcients involving nonlocal ...
Kemal Ozen, Kamil Orucoglu
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Unleashing the Power of Machine Learning in Nanomedicine Formulation Development
Advanced Functional Materials, EarlyView.A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore +7 more
wiley +1 more source
Applications of Bergman kernel functions [PDF]
, 2003 In this paper we revisit the so-called Bergman kernel method (BKM) for solving conformal mapping problems. This method is based on the reproducing property of the Bergman kernel function.
Bock, S., Falcão, M. I., Gürlebeck, K.
core
Advanced Functional Materials, EarlyView.Automat optical inspection (AOI) techniques in semiconductor fabrication can be leveraged in battery manufacturing, enabling scalable detection and analysis of electrode‐ and cell‐level imperfections through AI‐driven analytics and a digital‐twin framework.
Jianyu Li, Ertao Hu, Wei Wei, Feifei Shi
wiley +1 more source
, 2011 A nonparametric kernel-based method for realizing Bayes' rule is proposed, based on representations of probabilities in reproducing kernel Hilbert spaces. Probabilities are uniquely characterized by the mean of the canonical map to the RKHS.
Fukumizu, Kenji +2 more
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Reproducing kernel particle method and its modification [PDF]
Mathematica Bohemica, 2010 Summary: Meshless methods have become an effective tool for solving problems from engineering practice in last years. They have been successfully applied to problems in solid and fluid mechanics. One of their advantages is that they do not require any explicit mesh in computation. This is the reason why they are useful in the case of large deformations,
openaire +2 more sources
Photon Avalanching Nanoparticles: The Next Generation of Upconverting Nanomaterials?
Advanced Functional Materials, EarlyView.This Perspective outlines the mechanistic foundations that enable photon‐avalanche (PA) behavior in lanthanide nanomaterials and contrasts them with emerging application spaces and forward‐looking design strategies. By bridging threshold engineering, energy‐transfer dynamics, and materials engineering, we provide a coherent roadmap for advancing the ...
Kimoon Lee +7 more
wiley +1 more source
Numerical solution of potential problems using radial basis reproducing kernel particle method
Results in Physics, 2019 The paper presents the radial basis reproducing kernel particle method (RRKPM) for potential problems. The proposed RRKPM can eliminate the negative effect of different reproducing kernel functions (RKF) on computational stability and accuracy.
Hongfen Gao, Gaofeng Wei
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Fractal and Fractional, 2020 In this research, obtaining of approximate solution for fractional-order Burgers’ equation will be presented in reproducing kernel Hilbert space (RKHS). Some special reproducing kernel spaces are identified according to inner products and norms.
Onur Saldır +2 more
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