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 …

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 …

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

This work proposes an incremental learning (IL) framework for wearable sensor human
activity recognition (HAR) that tackles two challenges simultaneously: catastrophic forgetting …

Physics-Informed Neural Networks (PINNs) have emerged as a robust framework for solving
Partial Differential Equations (PDEs) by approximating their solutions via neural networks …

Time series forecasting is a crucial task that predicts the future values of variables based on
historical data. Time series forecasting techniques have been developing in parallel with the …

In this paper, we introduce FC-KAN, a Kolmogorov-Arnold Network (KAN) that leverages
combinations of popular mathematical functions such as B-splines, wavelets, and radial …

Load forecasting plays a crucial role in energy management, directly impacting grid stability,
operational efficiency, cost reduction, and environmental sustainability. Traditional Vanilla …