Results 101 to 110 of about 827,423 (299)
Advanced Intelligent Discovery, EarlyView.A physics‐guided machine learning framework estimates Young's modulus in multilayered multimaterial hyperelastic cylinders using contact mechanics. A semiempirical stiffness law is embedded into a custom neural network, ensuring physically consistent predictions. Validation against experimental and numerical data on C.
Christoforos Rekatsinas +4 more
wiley +1 more source
Newton's method in the context of gradients
Electronic Journal of Differential Equations, 2007 This paper gives a common theoretical treatment for gradient and Newton type methods for general classes of problems. First, for Euler-Lagrange equations Newton's method is characterized as an (asymptotically) optimal variable steepest descent method ...
John W. Neuberger, Janos Karatson
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A Fractional-Order Chemical System: Numerical Analysis with Distinct Variable-Order Derivatives
Journal of Mathematical Sciences and ModellingFractional calculus models complicated systems that exhibit memory effects, showing much greater potential than classical integer-order derivatives in modeling chaotic systems.
Ughur Budaq, Emrullah Yaşar
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Constrained regularization methods for ozone profile retrieval from UV/VIS nadir spectrometers
, 2009 In this paper we present several constrained regularization methods for ozone profile retrieval from UV/VIS nadir sounding instruments such as GOME, SCIAMACHY, OMI and GOME-2.
Doicu, Adrian +2 more
core +1 more source
Harnessing Machine Learning to Understand and Design Disordered Solids
Advanced Intelligent Discovery, EarlyView.This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley +1 more source
Advanced 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
ROBUST QUASI-NEWTON EQUATIONS IN QUASI-NEWTON METHOD FOR SOLVING UNCONSTRAINED OPTIMIZATION PROBLEMS
BarekengQuasi-Newton methods are among the most widely used and effective general-purpose algorithms for unconstrained optimization. These methods traditionally rely on the quasi-Newton equation, which serves as the foundation for updating approximations of the
Basim A. Hassan, Manal I. Mohammed
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Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.Quadrotor unmanned aerial vehicle control is critical to maintain flight safety and efficiency, especially when facing external disturbances and model uncertainties. This article presents a robust reinforcement learning control scheme to deal with these challenges.
Yu Cai +3 more
wiley +1 more source
An Efficient Direct Position Determination Method for Multiple Strictly Noncircular Sources
Sensors, 2018 This paper focuses on the localization methods for multiple sources received by widely separated arrays. The conventional two-step methods extract measurement parameters and then estimate the positions from them.
Jiexin Yin, Ding Wang, Ying Wu
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Inexact Newton-Kantorovich Methods for Constrained Nonlinear Model Predictive Control
, 2018 In this paper we consider Newton-Kantorovich type methods for solving control-constrained optimal control problems that appear in model predictive control. Conditions for convergence are established for an inexact version of the Newton-Kantorovich method
Nicotra, Marco +3 more
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