Results 91 to 100 of about 318 (192)

Two-Level Iteration Penalty Methods for the Navier-Stokes Equations with Friction Boundary Conditions

Abstract and Applied Analysis, 2013
This paper presents two-level iteration penalty finite element methods to approximate the solution of the Navier-Stokes equations with friction boundary conditions.
Yuan Li, Rong An
doaj   +1 more source

On a Newton type method

Journal of Numerical Analysis and Approximation Theory, 1994
Under Kantorovich type assumptions the author gives a convergence theorem for the methods proposed by \textit{S. Ul'm} [Izv. Akad. Nauk Ehston. SSR, Fiz. Mat. 16, 403-411 (1967; Zbl 0189.485)] and \textit{O. H. Hald} [Numer. Math. 23, 411-426 (1975; Zbl 0309.65020)] solving operator equations in Banach spaces. His a priori error bounds are sharper than
openaire   +3 more sources

A Physics Constrained Machine Learning Pipeline for Young's Modulus Prediction in Multimaterial Hyperelastic Cylinders Guided by Contact Mechanics

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, Panagiotis Krokidas, Vasileios Vavourakis, Clara Essmann, George Giannakopoulos   +4 more
wiley   +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

The Role of Chemistry Across Disciplines From Humanities to Life Sciences in Understanding Complexity and Emergence

Angewandte Chemie, EarlyView.
This study explores the origins of life by linking prebiotic chemistry, the emergence of information‐carrying molecules such as RNA and proteins, and philosophical questions about consciousness. The study emphasizes the role of molecular evolution in the Central Dogma and provides insights into the chemical origins of biology and the basis of life's ...
Harald Schwalbe, Josef Wachtveitl, Alexander Heckel, Florian Buhr, Sabrina Toews, Thomas M. Schimmer   +5 more
wiley   +2 more sources

Accelerating the convergence of Newton-type iterations

Journal of Numerical Analysis and Approximation Theory, 2017
In this paper, we present a new accelerating procedure in order to speed up the convergence of Newton-type methods. In particular, we derive iterations with a high and optimal order of convergence.
Tugal Zhanlav, Ochbadrakh Chuluunbaatar, Vandandoo Ulziibayar   +2 more
doaj   +2 more sources

Robust Reinforcement Learning Control Framework for a Quadrotor Unmanned Aerial Vehicle Using Critic Neural Network

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, Yefeng Yang, Tao Huang, Boyang Li   +3 more
wiley   +1 more source

NEW CONJUGATE GRADIENT METHOD FOR ACCELERATED CONVERGENCE AND COMPUTATIONAL EFFICIENCY IN UNCONSTRAINED OPTIMIZATION PROBLEMS

Barekeng
Conjugate gradient (CG) algorithms play an important role in solving large-scale unconstrained optimization problems due to their low memory requirements and strong convergence properties.
Basim A. Hassan, Alaa Luqman Ibrahim, Thaair A. Ameen, Ibrahim Mohammed Sulaiman   +3 more
doaj   +1 more source

Adaptive Autonomy in Microrobot Motion Control via Deep Reinforcement Learning and Path Planning Synergy

Advanced Intelligent Systems, EarlyView.
This study introduces a data‐driven framework that combines deep reinforcement learning with classical path planning to achieve adaptive microrobot navigation. By training a surrogate neural network to emulate microrobot dynamics, the approach improves learning efficiency, reduces training time, and enables robust real‐time obstacle avoidance in ...
Amar Salehi, Bairong Zhu, Haoyu Liu, Tingting Yu   +3 more
wiley   +1 more source

Modifications for Quasi-Newton Method and Its Spectral Algorithm for Solving Unconstrained Optimization Problems

مجلة بغداد للعلوم
In this paper, two modifications for spectral quasi-Newton algorithm of type BFGS are imposed. In the first algorithm, named SQNEI, a certain spectral parameter is used in such a step for BFGS algorithm differs from other presented algorithms.
Evar Lutfalla Sadraddin, Ivan Subhi Latif   +1 more
doaj   +1 more source