Results 141 to 150 of about 65,408 (307)
Laser &Photonics Reviews, EarlyView.Bayesian optimization combined with in situ quantitative phase imaging enables autonomous correction of layer‐height deviations in projection multi‐photon lithography. By jointly tuning model parameters and grayscale exposure settings, the method achieves more uniform and accurate layers within 300 prints, offering a fast, data‐efficient route to ...
Jason E. Johnson, Xianfan Xu
wiley +1 more source
Mathematical Methods in the Applied Sciences, EarlyView.ABSTRACT We develop a unified mathematical framework extending classical moment theory from discrete integer orders to a continuous spectrum of real orders f>0$$ f>0 $$, providing a systematic statistical characterization of complex systems exhibiting power‐law behavior.
Farrukh A. Chishtie
wiley +1 more source
On some inequalities of s-convex functions
, 2010 碩士在這篇論文中我們將介紹一些跟s-convex functions 有關的不等式。In this paper, we will introduce some inequalities about s-convex functions.Introduction 01 Main Results 03 Reference 12 導論 13 主要結果 15 參考文獻 24學號 ...
楊琪斌; Yang, Chi-pin
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International Journal for Numerical and Analytical Methods in Geomechanics, EarlyView.ABSTRACT This study proposes a novel geometrically regularized gradient‐damage model for simulating dynamic mixed‐mode fracture in orthotropic materials with tension–compression asymmetry. In this model, a thermodynamic framework is formulated by incorporating damage dissipation into internal energy evolution, from which the constitutive relation and ...
Hui Li, Shanyong Wang
wiley +1 more source
A Method For Approximating Univariate Convex Functions Using Only Function Value Evaluations
In this paper, piecewise linear upper and lower bounds for univariate convex functions are derived that are only based on function value information. These upper and lower bounds can be used to approximate univariate convex functions.
Siem, A.Y.D. +2 more
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Random gradient-free minimization of convex functions [PDF]
In this paper, we prove the complexity bounds for methods of Convex Optimization based only on computation of the function value. The search directions of our schemes are normally distributed random Gaussian vectors.
NESTEROV, Yurii
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A d.c. $C^1$ function need not be difference of convex $C^1$ functions [PDF]
, 2005 summary:In [2] a delta convex function on $\Bbb R^2$ is constructed which is strictly differentiable at $0$ but it is not representable as a difference of two convex function of this property.
Pavlica, David
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Polymer Composites, EarlyView.Overview of the holistic engineering lifecycle and core research pillars for wind turbine blades. ABSTRACT This paper reviews recent advancements across the lifecycle of wind turbine blades, focusing on three interconnected areas: advanced composites, structural optimization, and machine learning (ML) diagnostics. In materials, we highlight progress in
Kemal Hasirci +2 more
wiley +1 more source
Monotonicity Results for Arithmetic Means of Concave and Convex Functions
, 2006 By majorization approaches, some known results on monotonicity of the arithmetic means of convex and concave functions are proved and generalized once ...
Xu, Tie-Quan, Qi, Feng, Shi, Huan-Nan
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High‐Order Sliding‐Mode control for MIMO Systems
International Journal of Robust and Nonlinear Control, EarlyView.ABSTRACT This paper extends Lyapunov‐based homogeneous high‐order sliding‐mode control to a class of uncertain non‐square multi‐input multi‐output (MIMO) nonlinear systems with a well‐defined vector relative degree. The considered systems admit a normal‐form representation with an uncertain but full‐row‐rank input‐gain matrix.
Jaime A. Moreno, Angel Mercado‐Uribe
wiley +1 more source