Federated Kolmogorov-Arnold Networks for Health Data Analysis: A Study Using ECG Signal DOI
Sileshi Nibret Zeleke, Mario A. Bochicchio

2021 IEEE International Conference on Big Data (Big Data), Год журнала: 2024, Номер unknown, С. 8070 - 8077

Опубликована: Дек. 15, 2024

Язык: Английский

A comprehensive and FAIR comparison between MLP and KAN representations for differential equations and operator networks DOI
Khemraj Shukla, Juan Diego Toscano, Zhicheng Wang

и другие.

Computer Methods in Applied Mechanics and Engineering, Год журнала: 2024, Номер 431, С. 117290 - 117290

Опубликована: Авг. 19, 2024

Язык: Английский

Процитировано

23

Kolmogorov–Arnold PointNet: Deep learning for prediction of fluid fields on irregular geometries DOI
Ali Kashefi

Computer Methods in Applied Mechanics and Engineering, Год журнала: 2025, Номер 439, С. 117888 - 117888

Опубликована: Март 11, 2025

Язык: Английский

Процитировано

2

Three operator learning models for solving boundary integral equations in 2D connected domains DOI
Bin Meng, Yutong Lu, Ying Jiang

и другие.

Applied Mathematical Modelling, Год журнала: 2025, Номер unknown, С. 116034 - 116034

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

0

Energy-Dissipative Evolutionary Kolmogorov–Arnold Networks for Complex Pde Systems DOI
Guang Lin,

Changhong Mou,

Jiahao Zhang

и другие.

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0

The Application of the Novel Kolmogorov–Arnold Networks for Predicting the Fundamental Period of RC Infilled Frame Structures DOI Creative Commons
Shan Lin, Kaiyang Zhao,

Hongwei Guo

и другие.

International journal of mechanical system dynamics, Год журнала: 2025, Номер unknown

Опубликована: Март 18, 2025

ABSTRACT The fundamental period is a crucial parameter in structural dynamics that informs the design, assessment, and monitoring of structures to ensure safety stability buildings during earthquakes. Numerous machine‐learning deep‐learning approaches have been proposed predict infill‐reinforced concrete frame structures. However, challenges remain, including insufficient prediction accuracy excessive computational resource demands. This study aims provide new paradigm for accurately efficiently predicting periods, namely, Kolmogorov–Arnold networks (KANs) their variants, especially radial basis function KANs (RBF‐KANs). are formulated based on representation theorem, positioning them as promising alternative multilayer perceptron. In this research, we compare performance against fully connected neural (FCNNs) context prediction. mutual information method was employed analysis dependencies between features FP4026 data set. Nine predictive models, KANs, F‐KANs, FCNN‐2, FCNN‐11, CatBoost, Support Vector Machine, others, were constructed compared, with hyperparameters determined by Optuna, which will highlight optimal model amongst F‐KANs models. Numerical results manifest highest yielded R 2 = 0.9948, offers an explicit form formula. Lastly, further dive into explainability interpretability revealing number stories opening percentage significant effect results.

Язык: Английский

Процитировано

0

Physics-Informed Kolmogorov-Arnold Networks (PIKANs) for Solving the Buckley-Leverett Equation in Waterflooding Reservoirs DOI
Xiang Rao, Yongqian Liu, Xupeng He

и другие.

SPE Reservoir Simulation Conference, Год журнала: 2025, Номер unknown

Опубликована: Март 18, 2025

Abstract Kolmogorov-Arnold Networks (KANs), introduced in May 2024, present a novel network structure. Early researches show they outperform Multi-Layer Perceptrons (MLPs) computational efficiency, interpretability, and interaction. This paper aims to create the first physics-informed KAN (PIKAN) by replacing MLP with PINN, assessing its performance of solving fractional flow equation waterflooding reservoirs. To build PIKAN, spatial coordinates time serve as inputs, water saturation outputs. The loss function is derived from governing equation, initial, boundary conditions. It's optimized using Adam L-BFGS algorithms, updating PIKAN parameters. structure allows for automatic differentiation training, evaluation PIKANs conclude upon meeting accuracy criteria or reaching maximum optimization steps. We evaluate comparing their results high-fidelity benchmarks. findings reveal that can achieve similar prediction distribution MLP-based will experience more significant oscillations during training process compared PINN. In future, further improvement PIKAN's may be achieved improving optimizer study introduces promising into reservoir numerical simulations time, achieving pressure modeling heterogeneous reservoirs PIKAN. Compared existing developed demonstrates superior accuracy, robustness. provide initial reference developing universal rapid simulation history matching tools based on

Язык: Английский

Процитировано

0

Extraction and reconstruction of variable-coefficient governing equations using Res-KAN integrating sparse regression DOI
Maozu Guo,

Xing Lü,

Yongtao Jin

и другие.

Physica D Nonlinear Phenomena, Год журнала: 2025, Номер unknown, С. 134689 - 134689

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

0

Intelligent monitoring of impact damage within concrete through deep learning-empowered electromechanical impedance technique DOI
Qixiang Yan, Yingxin Yang, Chuan Zhang

и другие.

Measurement, Год журнала: 2025, Номер unknown, С. 117642 - 117642

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

0

Adaptive Method for Selecting Basis Functions in Kolmogorov–Arnold Networks for Magnetic Resonance Image Enhancement DOI
M. A. Penkin, A. S. Krylov

Programming and Computer Software, Год журнала: 2025, Номер 51(3), С. 167 - 172

Опубликована: Май 11, 2025

Язык: Английский

Процитировано

0

PDS-UKAN: Subdivision hopping connected to the U-KAN network for medical image segmentation DOI
Liwei Deng, Wenbo Wang,

Songyu Chen

и другие.

Computerized Medical Imaging and Graphics, Год журнала: 2025, Номер 124, С. 102568 - 102568

Опубликована: Май 23, 2025

Язык: Английский

Процитировано

0