Results 61 to 70 of about 12,439,103 (299)

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

Quasi-Newton methods for generalized equations

, 1979
Newton's method is a well known and often applied technique for computing a zero of a nonlinear function. Situations arise in which it is undesirable to evaluate, at each iteration, the derivative appearing in the Newton iteration formula. In these cases,
Josephy, Norman
core   +2 more sources

Generalized Newton Method for a Kind of Complementarity Problem

, 2020
A generalized Newton method for the solution of a kind of complementarity problem is given. The method is based on a nonsmooth equations reformulation of the problem by F-B function and on a generalized Newton method.
Shou-Qiang Du
core  

Inexact Newton’s method for generalized operator equations in Banach spaces [PDF]

, 2018
In the present paper, we introduce a new inexact Newton-like algorithm for solving the generalized operator equations containing non differentiable operators in Banach space setting and discuss its semilocal convergence analysis under the weak Lipschitz ...
Vipin Kumar Singh, Singh, V. K.
core   +2 more sources

DeepMapper: Attention‐Based AutoEncoder for System Identification in Wound Healing and Stage Prediction

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, Ksenia Zlobina, Michael Briden, Oscar Avalos, Héctor Carrión, Sebastian Osorio, Hsin‐ya Yang, Alexandra Nava, Michelle D. Bagood, Marco Rolandi, Roslyn Rivkah Isseroff, Marcella Gomez   +11 more
wiley   +1 more source

Computational Contact Problems: Investigations on the Robustness of Generalized Newton Method, Fixed-Point method and Partial Newton Method

, 2018
International audienceDealing with frictional contact simulations, one can use generalized Newton method or Fixed-point method. In this article, we present partial Newton method which combines advantages from both algorithms.
Lebon, Frédéric, De-Soza, Thomas, Kudawoo, Ayaovi Dzifa, Rosu, Iulian, Abbas, Mickaël   +4 more
core   +1 more source

Crystal Structure Prediction of Inorganic Materials: A Benchmark and Modern Evaluation

Advanced Intelligent Discovery, EarlyView.
Predicting a crystal’s structure from composition alone is a long‐standing challenge in materials discovery. The CSP180 benchmark of 180 inorganic crystals evaluates thirteen crystal structure prediction algorithms requiring no density functional theory (DFT) against DFT‐based baselines across twelve metrics.
Lai Wei, Sadman Sadeed Omee, Rongzhi Dong, Nihang Fu, Yuqi Song, Edirisuriya M. Dilanga Siriwardane, Meiling Xu, Ming Hu, Chris Wolverton, Jianjun Hu   +9 more
wiley   +1 more source

Block Preconditioning Matrices for the Newton Method to Compute the Dominant λ-Modes Associated with the Neutron Diffusion Equation

Mathematical and Computational Applications, 2019
In nuclear engineering, the λ -modes associated with the neutron diffusion equation are applied to study the criticality of reactors and to develop modal methods for the transient analysis. The differential eigenvalue problem that needs to be
Amanda Carreño, Luca Bergamaschi, Angeles Martinez, Antoni Vidal-Ferrándiz, Damian Ginestar, Gumersindo Verdú   +5 more
doaj   +1 more source

A semismooth newton method for the nearest Euclidean distance matrix problem

, 2013
The Nearest Euclidean distance matrix problem (NEDM) is a fundamentalcomputational problem in applications such asmultidimensional scaling and molecularconformation from nuclear magnetic resonance data in computational chemistry.Especially in the latter ...
Qi, Houduo
core   +1 more source

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