Abstract Kolmogorov–Arnold Networks (KANs) were recently introduced as an alternative
representation model to MLP. Herein, we employ KANs to construct physics-informed …

The modern digital engineering design often requires costly repeated simulations for
different scenarios. The prediction capability of neural networks (NNs) makes them suitable …

Abstract Kolmogorov–Arnold networks (KANs) as an alternative to multi-layer perceptrons
(MLPs) are a recent development demonstrating strong potential for data-driven modeling …

A major challenge of AI+ Science lies in their inherent incompatibility: today's AI is primarily
based on connectionism, while science depends on symbolism. To bridge the two worlds …

This systematic review explores the theoretical foundations, evolution, applications, and
future potential of Kolmogorov-Arnold Networks (KAN), a neural network model inspired by …

Kolmogorov-Arnold Networks (KAN) is a groundbreaking model recently proposed by the
MIT team, representing a revolutionary approach with the potential to be a game-changer in …

In recent years, Graph Neural Networks (GNNs) have become the de facto tool for learning
node and graph representations. Most GNNs typically consist of a sequence of …

Quantum architecture Search (QAS) is a promising direction for optimization and automated
design of quantum circuits towards quantum advantage. Recent techniques in QAS …

In this research, we propose the first approach for integrating the Kolmogorov-Arnold
Network (KAN) with various pre-trained Convolutional Neural Network (CNN) models for …

The development of Kolmogorov-Arnold networks (KANs) marks a significant shift from
traditional multi-layer perceptrons in deep learning. Initially, KANs employed B-spline curves …