Dynamical and Computational Analysis of Fractional Korteweg–de Vries-Burgers and Sawada–Kotera Equations in Terms of Caputo Fractional Derivative by N. S. Alharthi

“…The homotopy perturbation transform method (HPTM) and the Yang transform decomposition method (YTDM) are two sophisticated techniques employed to derive analytical solutions. …”
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A Reduced-Order Algorithm for a Digital Twin Model of Ultra-High-Voltage Valve-Side Bushing Considering Spatio-Temporal Non-Uniformity by Yongsheng He, Yongfu Li, Xiangcheng Li, Yanan Yuan, Fan Yang, Zongxiang Lu

“…This proposed method reduces the calculation time to 10% of the full-order simulation model while controlling the error range of the key research area less than 0.1%. …”
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Image Characteristic-Guided Learning Method for Remote-Sensing Image Inpainting by Ying Zhou, Xiang Gao, Xinrong Wu, Fan Wang, Weipeng Jing, Xiaopeng Hu

“…Additionally, IGLL incorporates mathematical constraints into deep-learning models. A singular value decomposition (SVD) loss item is proposed to model the low-rankness characteristic, and it constrains feature consistency. …”
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Forecasting Influenza Trends Using Decomposition Technique and LightGBM Optimized by Grey Wolf Optimizer Algorithm by Yonghui Duan, Chen Li, Xiang Wang, Yibin Guo, Hao Wang

“…The residual sequence from the GWO-LightGBM model was then decomposed and corrected using the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method, which led to the development of the GWO-LightGBM-CEEMDAN model. …”
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Modeling population size independent tissue epigenomes by ChIL‐seq with single thin sections by Kazumitsu Maehara, Kosuke Tomimatsu, Akihito Harada, Kaori Tanaka, Shoko Sato, Megumi Fukuoka, Seiji Okada, Tetsuya Handa, Hitoshi Kurumizaka, Noriko Saitoh, Hiroshi Kimura, Yasuyuki Ohkawa

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Accelerated Convergence Method for Flow Field Based on DMD-POD Combined Reduced-Order Optimization Model by Jianhui Li, Jun Huang, Yahui Sun, Guoqiang Li

“…This method involves conducting dynamic mode decomposition (DMD) and proper orthogonal decomposition (POD) model reduction on the field snapshots. …”
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Generalized Logarithmic Tensor Nuclear Norm for Hyperspectral-Multispectral Image Fusion via Tensor Ring Decomposition by Jun Zhang, Mengling He, Chengzhi Deng

“…To address these issues, we propose a new HSI–MSI fusion model based on the generalized logarithmic tensor nuclear norm (GLTNN) under the TR decomposition framework. …”
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Enhanced Workload Prediction in Data Centers Using Two-Stage Decomposition and Hybrid Parallel Deep Learning by Dalal Alqahtani, Hamidreza Imani, Tarek El-Ghazawi

“…To improve this, we introduce CVCBM which blends signal processing techniques Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Variational Mode Decomposition (VMD) with advanced deep learning models like Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) networks. …”
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A double broad learning approach based on variational modal decomposition for Lithium-Ion battery prognostics by Xiaojia Wang, Xinyue Guo, Sheng Xu, Xibin Zhao

“…Therefore, in this paper, a novel model based on variational modal decomposition and double broad learning (VMD-DBL) is proposed. …”
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Domain Knowledge Decomposition for Cross-Domain Few-Shot Scene Classification From Remote Sensing Imagery by Can Li, He Chen, Yin Zhuang, Liang Chen, Lianlin Li

“…Hence, in this article, a novel CDFSSC method called domain knowledge decomposition (DKD) framework is proposed to effectively exploit domain-common and domain-specific knowledge from the pseudo-labels of target samples, improve the certainty of cross-domain representation learning, and enhance the model’s adaptability to the target domain. …”
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Combining Kronecker-Basis-Representation Tensor Decomposition and Total Variational Constraint for Spectral Computed Tomography Reconstruction by Xuru Li, Kun Wang, Yan Chang, Yaqin Wu, Jing Liu

“…The method based on tensor decomposition can effectively remove noise by exploring the correlation of energy channels, but it is difficult for traditional tensor decomposition methods to describe the problem of tensor sparsity and low-rank properties of all expansion modules simultaneously. …”
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Socioeconomic Inequality in Chronic Complications of Type 2 Diabetes Mellitus in Iran: Concentration Index and Decomposition Approach by Sedigheh Mafakheri, Erfan Ayubi, Shiva Borzouei, Vajiheh Ramezani Doroh, Salman Khazaei

“…A binary logistic regression model was utilized to investigate the relationship between diabetes complications and independent variables. …”
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Analysis Method of Agricultural Total Factor Productivity Based on Stochastic Block Model (SBM) and Machine Learning by Yanzi Li, Cai Chen, Fuqiang Liu, Jian Wang

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An Intelligent Framework for Multiscale Detection of Power System Events Using Hilbert–Huang Decomposition and Neural Classifiers by Juan Vasquez, Manuel Jaramillo, Diego Carrión

“…The proposed model achieved a classification accuracy of 94.09% and demonstrated consistent performance across all time windows, supporting its suitability for real-time monitoring in smart distribution networks. …”
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Fault Diagnosis of Axial Piston Pump Based on Extreme-Point Symmetric Mode Decomposition and Random Forests by Lei Yafei, Jiang Wanlu, Niu Hongjie, Shi Xiaodong, Yang Xukang

“…Aiming at fault diagnosis of axial piston pumps, a new fusion method based on the extreme-point symmetric mode decomposition method (ESMD) and random forests (RFs) was proposed. …”
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Combined CNN-BiLSTM-Att tourism flow prediction based on VMD-MWPE decomposition reconstruction by Maomao Luo, Danhong Chen, Xinxing Hou, Kang Luo, Tao Wang

“…These statistically significant improvements validate the model’s superior predictive capability for tourist flow forecasting. …”
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DYNAMIC UNBALANCE DETECTION OF CARDAN SHATF IN HIGH-SPEED TRAIN BASED ON MODIFIED VARIATIONAL MODE DECOMPOSITION by HONG JianFeng, DING JianMing, LIN JianHui, ZHENG ShuBin

“…With comparison to the classic ensemble empirical model decomposition,the fault detection ability has been significantly improved.…”
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Integrating Time Series Decomposition and Deep Learning: An STL-TCN-Transformer Framework for Landslide Displacement Prediction by Shuai Ren, Kamarul Hawari Ghazali

“…This study proposes an STL-TCN-Transformer model that combines time series decomposition with deep learning to predict cumulative displacement. …”
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Investigation of the Effects of Length to Depth Ratio on Open Supersonic Cavities Using CFD and Proper Orthogonal Decomposition by Ibrahim Yilmaz, Ece Ayli, Selin Aradag

“…Two-dimensional compressible time-dependent Reynolds-averaged Navier-Stokes equations with k-ω turbulence model are solved. A reduced order modeling approach, Proper Orthogonal Decomposition (POD) method, is used to further analyze the flow. …”
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Optimization of fluid control laws through deep reinforcement learning using dynamic mode decomposition as the environment by T. Sakamoto, K. Okabayashi

“…In this study, we examine the feasibility of deriving an effective control law using a reduced-order model constructed by dynamic mode decomposition with control (DMDc). …”
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