Numerical simulation study on the evolution of wrinkling defects in carbon fiber laminates based on spatial decomposition damage variable method by ZHENG Haocheng, ZHOU Bo, LI Hui, WANG Yajie, SUN Ning, ZHANG Xueyan

“…In order to investigate the compression damage evolution of carbon fiber laminates with wrinkles and accurately predict the mechanical behavior of damage initiation and propagation, a progressive damage finite element model was proposed based on three-dimensional elastic theory by employing a spatial decomposition of damage variables method to establish the damage constitutive relation. …”
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Ultimate Compression: Joint Method of Quantization and Tensor Decomposition for Compact Models on the Edge by Mohammed Alnemari, Nader Bagherzadeh

“…This paper proposes the “ultimate compression” method as a solution to the expansive computation and high storage costs required by state-of-the-art neural network models in inference. …”
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Decomposition method in the study load-loading forestry manipulator mechanism mathematical model by Uskov Vladimir, Raetskaya Elena, Popikova Alina

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A Novel Separable Model and Decomposition Method for Sensor Locational Decision Problem by Linfeng Yang, Jie Li, Jin Ye, Zhigang Zhao

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Numerical benchmarking of dual decomposition-based optimization algorithms for distributed model predictive control by Vassilios Yfantis, Achim Wagner, Martin Ruskowski

Subjects: “…Distributed model predictive control…”
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Analysis Approximate with Using Sumudu Adomian Decomposition Method for Solving SEIVR Epidemic Model by Zainab Mohammed Jouhda, Saad N. Ali AL-Azzawi

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Surrogate Model for In-Medium Similarity Renormalization Group Method Using Dynamic Mode Decomposition by Sota Yoshida

“…I propose a data-driven surrogate model for the In-Medium Similarity Renormalization Group (IMSRG) method using Dynamic Mode Decomposition (DMD). …”
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Impact of Different Mode Decomposition Methods Combined with LSTM Models on Daily Runoff Forecasting by TAN Yongjie, WANG Xianxun, DUAN Mingxu, LIU Yaru, YAO Huaming

“…A combination of modal decomposition and deep learning forecasting methods was introduced to daily runoff forecasting to address the characteristics of unstable and volatile daily runoff series.Firstly,the complete ensemble empirical modal decomposition method was used to decompose the daily runoff time series,so as to obtain the modal components of different frequency components.Secondly,the daily runoff forecasting model was constructed for different modal components based on the long short-term memory neural network (LSTM),and the hyperparameters of the forecasting model were optimized using the grid search parametric optimization algorithm.Finally,the forecasting results of each model were modally reconstructed to obtain daily runoff forecasting results.The daily runoff forecasting of the Yichang hydrological station was taken as an example.Compared with the single LSTM,the RMSE,MAE,and MAPE of the proposed combination model were reduced by 65.02%,58.35%,and 2.88%,respectively.The decomposition effect of the complete ensemble empirical mode decomposition was better than that of the traditional modal decomposition method,which provided a new method and reference for nonlinear and non-stable daily runoff forecasting in a short time scale.…”
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An Improved General Five-Component Scattering Power Decomposition Method by Yu Wang, Daqing Ge, Bin Liu, Weidong Yu, Chunle Wang

“…The resulting transformed coherency matrices are then subjected to a five-component decomposition framework, enhanced with four refined volume scattering models. …”
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Applicability Analysis of Reduced-Order Methods with Proper Orthogonal Decomposition for Neutron Diffusion in Molten Salt Reactor by Zhengyang Zhou, Ming Lin, Maosong Cheng, Yuqing Dai, Xiandi Zuo

“…A set of reduced-order modeling frameworks based on Proper Orthogonal Decomposition (POD) is developed to improve the computational efficiency for neutron diffusion calculations while maintaining accuracy, especially for small samples. …”
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A Time Series Decomposition-Based Interpretable Electricity Price Forecasting Method by Yuanke Cu, Kaishu Wang, Lechen Zhang, Zixuan Liu, Yixuan Liu, Li Mo

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A Hybrid Solvers Enhanced Integral Equation Domain Decomposition Method for Modeling of Electromagnetic Radiation by Ran Zhao, Jun Hu, Han Guo, Ming Jiang, Zai-ping Nie

“…The hybrid solvers based on integral equation domain decomposition method (HS-DDM) are developed for modeling of electromagnetic radiation. …”
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A Multinomial Ordinal Probit Model with Singular Value Decomposition Method for a Multinomial Trait by Soonil Kwon, Mark O. Goodarzi, Kent D. Taylor, Jinrui Cui, Y.-D. Ida Chen, Jerome I. Rotter, Willa Hsueh, Xiuqing Guo

“…We developed a multinomial ordinal probit model with singular value decomposition for testing a large number of single nucleotide polymorphisms (SNPs) simultaneously for association with multidisease status when sample size is much smaller than the number of SNPs. …”
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Application of the Natural Decomposition Method to Strongly Coupled Partial Differential Equations: The Gray-Scott Model. by Nsingwire, Godwin

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Modified Mathematical Model of Tumor Treatment by Radiotherapy by Saad Naji AL- Azzawi, Fatima Ahmed Shihab

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To the solution of geoelectric problem on a direct current based on the global decomposition by N. A. Sevostianov, M. N. Yudin

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Thermal Properties of Short Fibre Composites Modeled by Meshless Method by Vladimír Kompiš, Zuzana Murčinková

“…Computational model using continuous source functions along the fibre axis is presented for simulation of temperature/heat flux in composites reinforced by short fibres with large aspect ratio. …”
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Optimization method of building energy efficiency design based on decomposition multi objective and agent assisted model by Bai Chaoqin, Yang Zhuoyue

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Study on nonlinear asymmetric thermomechanical stability of microsize FGM curved beams based on nonlocal couple stress curvature sensitive model by Saeid Sahmani, Kamila Kotrasova, Muhammad Atif Shahzad, Veronika Valaskova, Mona Zareichian, Babak Safaei

“…To originate the numerical curvature sensitive model, the radial point interpolation meshfree technique is utilized embracing the variation of the nodal points density based upon the background decomposition method (BDM). …”
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A novel LLM time series forecasting method based on integer-decimal decomposition by Lei Wang, Keyao Dong, Xiaoyong Zhao

“…Inspired by advancements in natural language processing (NLP) and computer vision (CV), large language models (LLMs) have emerged as a promising method for time series forecasting. …”
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