One-layer neural network for solving least absolute deviation problem with box and equality constraints
Sign Up to like & getrecommendations! Published in 2019 at "Neurocomputing"
DOI: 10.1016/j.neucom.2018.11.037
Abstract: Abstract This paper presents a neural network for solving least absolute deviation problems with equality and box constraints. Compared with some existing models, the proposed neural network has fewer state variables and only one-layer structure.… read more here.
Keywords: absolute deviation; network; solving least; network solving ... See more keywords
Kolmogorov–Arnold Network for Solving 2-D Magnetostatic Problems
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Transactions on Magnetics"
DOI: 10.1109/tmag.2025.3547064
Abstract: The data-driven machine-learning approach has significantly advanced the development of computational electromagnetics. This study introduces the Kolmogorov-Arnold network (KAN) as a novel method to overcome the limitations of traditional multilayer perceptron-based physics-informed neural networks (MLP-PINNs),… read more here.
Keywords: solving magnetostatic; kolmogorov arnold; magnetostatic problems; arnold network ... See more keywords