Results 51 to 60 of about 11,022,240 (248)

MODELING OF KOVAZHNY FLOW AND TAYLOR – GREEN VORTEX ON PHYSICS-INFORMED RADIAL BASIS FUNCTION NETWORKS

Модели, системы, сети в экономике, технике, природе и обществе
Background. An analysis of physics-informed neural networks for solving partial differential equations has been conducted, and the advantages of physics-informed radial basis function networks have been demonstrated.
Dmitry A. Stenkin
doaj   +1 more source

Leveraging Symbolic Artificial Intelligence and Fuzzy Logic for Materials Science: A Review of Methods, Challenges, and Applications to Scarce and Imperfect Experimental Data

Advanced Engineering Materials, EarlyView.
This article explores the transformative potential of symbolic artificial intelligence (AI) in the field of materials science, particularly in leveraging experimental data. The article presents several symbolic AI models and discusses their applications in materials science.
Ahmed Amrani, Mohamed‐Ali Belloum, Laurence Boudet, Valentin Fouillard, Edwin Friedmann, Guillaume Lacharme, Ali Mahmoud, Jean‐Philippe Poli   +7 more
wiley   +1 more source

RbfCon: Construct Radial Basis Function Neural Networks with Grammatical Evolution

Software
Radial basis function networks are considered a machine learning tool that can be applied on a wide series of classification and regression problems proposed in various research topics of the modern world.
Ioannis G. Tsoulos, Ioannis Varvaras, Vasileios Charilogis   +2 more
doaj   +1 more source

A tunable radial basis function model for nonlinear system identification using particle swarm optimisation [PDF]

, 2009
A tunable radial basis function (RBF) network model is proposed for nonlinear system identification using particle swarm optimisation (PSO). At each stage of orthogonal forward regression (OFR) model construction, PSO optimises one RBF unit's centre ...
Harris, C. J., X. Hong, B.L. Luk, Chen, Sheng, C.J. Harris, Harris, Chris J., Harris, C.J., Chen, S., Luk, Bing L., Hong, Xia, S. Chen, Luk, B. L., Hong, X.   +12 more
core   +1 more source

High‐Entropy Alloy Interlayers Toward Advanced Joining for High‐Performance Structural Applications: Current Progress and Emerging Challenges

Advanced Engineering Materials, EarlyView.
HEA interlayers offer a versatile route for joining high‐performance structural materials. Their compositional and structural design regulates interfacial reactions, suppresses brittle IMCs, and improves metallurgical bonding. Sandwich interlayers further integrate defect healing with precipitation strengthening, enabling improved strength–ductility ...
Lin Yuan, Jiafen Song, Jinglong Li, Jiangtao Xiong, Tao Yang   +4 more
wiley   +1 more source

Reconstruction of Daily Sea Surface Temperature Based on Radial Basis Function Networks

Remote Sensing, 2017
A radial basis function network (RBFN) method is proposed to reconstruct daily Sea surface temperatures (SSTs) with limited SST samples. For the purpose of evaluating the SSTs using this method, non-biased SST samples in the Pacific Ocean (10°N–30°N, 115°
Zhihong Liao, Qing Dong, Cunjin Xue, Jingwu Bi, Guangtong Wan   +4 more
doaj   +1 more source

Supporting AI Readiness Through Digital Workflows in Materials Science

Advanced Engineering Materials, EarlyView.
Digitalization drives innovation in materials science by connecting data silos and turning heterogeneous processes into reusable research pipelines. Across 13 MaterialDigital projects, digital workflows reveal complementary pathways toward AI‐ready materials research, founded on structured data, persistent artifacts, executable orchestration, and ...
Marian Bruns, Lateef Abdul, Stephan Barth, Martin Glauer, Manuel Günther, Fabian Neuhaus, Jan‐Ole Perschewski, Thomas Bjarsch, Henrik Abel, Johannes Bosch, Niklas Meyer, Peter Hofmann, Marc Drechsler, Alexander Dyck, Dhanunjaya Kumar Nerella, Marvin Tegeler, Oleg Shchyglo, Ingo Steinbach, Timo Klecker, Henk Birkholz, Ehsan Borzabadi Farahani, Bernard Fedelich, Cetin Haftaoglu, Tarakeshwar Lakshmipathy, Elena Garcia Trelles, Christoph Schweizer, Reza Darvishi Kamachali, Lukas Koschmieder, Janin Eiken, Markus Apel, Sung‐Min Wi, Jiangdong Zhao, Andreas Fezer, Nicolas Christ, Patrick Schneider, Eldor Urinov, Veit Königer, Volker Knoblauch, Janik Harter, Thomas Seifert, Thitichai Janpheng, Florian Fuchs, Fabian Teichert, Jörg Schuster, Anke Bardehle, Andreas Limper, Ulrich Giese, Carlos Lucke, Mario Beiner, Christoph Gögelein, Lisa Leuchtenberger‐Engel, Christian Hopmann, Felix Wentzien, Heike Wittek, Benjamin Klie, Alexander Röhrs, Ludger Overmeyer, Achraf Atila, Marcel Sadowski, Leopold Talirz, Jan Janssen, Jesper R. Pedersen, Peter Beck, Tejs Vegge, Ivano E. Castelli, Wolfgang Wenzel, Tilmann Hickel, Jörg Schaarschmidt   +67 more
wiley   +1 more source

A Novel Deep Reinforcement Learning Based Extended Fractal Radial Basis Function Network for State‐of‐Charge Estimation

IET Power Electronics
This paper presents a novel deep reinforcement learning based extended fractal radial basis function (DRL‐EFRBF) network for accurate state‐of‐charge (SOC) estimation in lithium iron phosphate (LiFePO4) batteries. Unlike conventional methods such as open
Syed M. Ali, Muhammad Bilal, Ayman Alharbi, Rooh ul Amin   +3 more
doaj   +1 more source

Electroencephalography Artifact Removal using Optimized Radial Basis Function Neural Networks

Majlesi Journal of Electrical Engineering
Electroencephalography (EEG) is a major clinical tool to diagnose, monitor and manage neurological disorders which is mostly affected by artifacts.
Shoorangiz Shams Shamsabad Farahani, Mohammad Mahdi Arefi, Amir Hossein Zaeri   +2 more
doaj   +1 more source

Orthogonal least squares algorithm for training multi-output radial basis function networks

, 1992
A constructive learning algorithm for multioutput radial basis function networks is presented. Unlike most network learning algorithms, which require a fixed network structure, this algorithm automatically determines an adequate radial basis function ...
Cowan, C. F. N., Grant, P. M., Chen, S.
core