Results 81 to 90 of about 84,313 (308)
A feed forward neural network approach for matrix computations [PDF]
, 2001 This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.A new neural network approach for performing matrix computations is presented. The idea of this approach is to construct a feed-forward neural network (FNN)
Al-Mudhaf, Ali F, Al-Mudhaf, A.F.
core
Modelling stem cell differentiation related processes—A practical overview for biologists
FEBS Letters, EarlyView.Stem cell differentiation is complex and difficult to control experimentally. This review introduces suitable computational modelling approaches that can support stem cell research, from mechanistic ODE and abstract models to multiscale and deep learning methods.
Ricco Zeegelaar +4 more
wiley +1 more source
Least-squares-based three-term conjugate gradient methods
Journal of Inequalities and Applications, 2020 In this paper, we first propose a new three-term conjugate gradient (CG) method, which is based on the least-squares technique, to determine the CG parameter, named LSTT.
Chunming Tang, Shuangyu Li, Zengru Cui
doaj +1 more source
CONJUGATE GRADIENTS PARALLELIZED ON THE HYPERCUBE
International Journal of Modern Physics C, 1993 For the solution of discretized ordinary or partial differential equations it is necessary to solve systems of equations with coefficient matrices of different sparsity pattern, depending on the discretization method; using the finite element method (FE) results in largely unstructured systems of equations.
openaire +2 more sources
The conjugate gradient method [PDF]
, 2018 The conjugate gradient method can be used to solve many large linear geophysical problems — for example, least-squares parabolic and hyperbolic Radon transform, traveltime tomography, least-squares migration, and full-waveform inversion (FWI) (e.g ...
Karl Schleicher
core +1 more source
Investigating transcription factor dynamics in health and disease using FRAP
FEBS Letters, EarlyView.FRAP analysis of GFP‐tagged transcription factors reveals how molecular mobility and target engagement change in response to drug treatment. By combining live‐cell imaging, quantitative model fitting, and statistical analysis, this approach uncovers transcription factor dynamics linked to disease mechanisms, providing a powerful framework for ...
Kannan Govindaraj +3 more
wiley +1 more source
A hardware acceleration technique for gradient descent and conjugate gradient [PDF]
, 2011 Gradient descent, conjugate gradient, and other iterative algorithms are a powerful class of algorithms; however, they can take a long time for conver- gence.
Kesler, David R.
core
A self-scaling memoryless BFGS based conjugate gradient method using multi-step secant condition for unconstrained minimization [PDF]
summary:Conjugate gradient methods are widely used for solving large-scale unconstrained optimization problems, because they do not need the storage of matrices.
Kim, Yong, Kim, Yongjin, Jong, Yunchol
core +1 more source
Conserved binding mode but diverse interfaces of MreC‐PBP2 interactions
FEBS Letters, EarlyView.The crystal structure of abMreC reveals a conserved two β‐barrel architecture and provides structural insights into its role within the bacterial elongasome. The abMreC–abPBP2 complex model identifies the molecular basis of MreC‐mediated PBP2 recognition, contributing to the regulation of peptidoglycan synthesis.
Hyunseok Jang +4 more
wiley +1 more source
Generalized conjugate gradient squared
Journal of Computational and Applied Mathematics, 1996 A generalized conjugate gradient squared method for solving nonsymmetric linear systems of equations is proposed. Instead of squaring the Bi-CG polynomial as in the conjugate gradient squared (CGS) method, the authors use a product of two nearby Bi-CG polynomials.
Fokkema, D.R. +2 more
openaire +3 more sources