Bindi Saurabh Thakkar¹, Pradeep Kumar Karsh^2,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-22, 2026, DOI:10.32604/cmc.2025.072839 - 09 December 2025

Abstract This study investigates the uncertain dynamic characterization of hybrid composite plates by employing advanced machine-assisted finite element methodologies. Hybrid composites, widely used in aerospace, automotive, and structural applications, often face variability in material properties, geometric configurations, and manufacturing processes, leading to uncertainty in their dynamic response. To address this, three surrogate-based machine learning approaches like radial basis function (RBF), multivariate adaptive regression splines (MARS), and polynomial neural networks (PNN) are integrated with a finite element framework to efficiently capture the stochastic behavior of these plates. The research focuses on predicting the first three natural frequencies… More >

Xing Lin¹, Junning Wu¹, Shixue Liang^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 215-239, 2025, DOI:10.32604/cmes.2025.070846 - 30 October 2025

Abstract Random media like concrete and ceramics exhibit stochastic crack propagation due to their heterogeneous microstructures. This study establishes a Conditional Generative Adversarial Network (CGAN) combined with random field modeling for the efficient prediction of stochastic crack patterns and stress-strain responses. A total dataset of 500 samples, including crack propagation images and corresponding stress-strain curves, is generated via random Finite Element Method (FEM) simulations. This dataset is then partitioned into 400 training and 100 testing samples. The model demonstrates robust performance with Intersection over Union (IoU) scores of 0.8438 and 0.8155 on training and testing datasets, More >

Feng He^*, Rui Tan, Songlian Jiang, Chao Qian, Chengzhong Bu, Benqiang Wang

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.10, pp. 2557-2577, 2025, DOI:10.32604/fdmp.2025.070186 - 30 October 2025

Abstract This study introduces a Transformer-based multimodal fusion framework for simulating multiphase flow and heat transfer in carbon dioxide (CO₂)–water enhanced geothermal systems (EGS). The model integrates geological parameters, thermal gradients, and control schedules to enable fast and accurate prediction of complex reservoir dynamics. The main contributions are: (i) development of a workflow that couples physics-based reservoir simulation with a Transformer neural network architecture, (ii) design of physics-guided loss functions to enforce conservation of mass and energy, (iii) application of the surrogate model to closed-loop optimization using a differential evolution (DE) algorithm, and (iv) incorporation of economic… More >

Yi Guan¹, Pengpeng Zhi^2,3,*, Zhonglai Wang^1,4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3343-3368, 2025, DOI:10.32604/cmes.2025.069515 - 30 September 2025

Abstract Variable-fidelity (VF) surrogate models have received increasing attention in engineering design optimization as they can approximate expensive high-fidelity (HF) simulations with reduced computational power. A key challenge to building a VF model is devising an adaptive model updating strategy that jointly selects additional low-fidelity (LF) and/or HF samples. The additional samples must enhance the model accuracy while maximizing the computational efficiency. We propose ISMA-VFEEI, a global optimization framework that integrates an Improved Slime-Mould Algorithm (ISMA) and a Variable-Fidelity Expected Extension Improvement (VFEEI) learning function to construct a VF surrogate model efficiently. First, A cost-aware VFEEI More >

Ying Zhou¹, Jiayang Yuan¹, Zhengtao Shu¹, Mengli Ye¹, Liang Gao¹, Qiong Wang^2,*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 557-580, 2025, DOI:10.32604/cmc.2025.067761 - 29 August 2025

Abstract Simultaneously, reducing an acoustic metamaterial’s weight and sound pressure level is an important but difficult topic. Considering the law of mass, traditional lightweight acoustic metamaterials make it difficult to control noise efficiently in real-life applications. In this study, a novel optimization-driven design scheme is developed to obtain lightweight acoustic metamaterials with a strong sound insulation capability for additive manufacturing. In the proposed design scheme, a topology optimization method for an acoustic metamaterial in the acoustic-solid interaction system is implemented to obtain an initial cross-sectional topology of the acoustic microstructure during the conceptual design phase. Then, More >

Yizhe Han^1,2, Guangjing Huang¹, Fei Xiao¹, Zhiyin Huang^3,*, Yuting Dai¹

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.7, pp. 1657-1671, 2025, DOI:10.32604/fdmp.2025.067528 - 31 July 2025

Abstract During high-speed forward flight, helicopter rotor blades operate across a wide range of Reynolds and Mach numbers. Under such conditions, their aerodynamic performance is significantly influenced by dynamic stall—a complex, unsteady flow phenomenon highly sensitive to inlet conditions such as Mach and Reynolds numbers. The key features of three-dimensional blade stall can be effectively represented by the dynamic stall behavior of a pitching airfoil. In this study, we conduct an uncertainty quantification analysis of dynamic stall aerodynamics in high-Mach-number flows over pitching airfoils, accounting for uncertainties in inlet parameters. A computational fluid dynamics (CFD) model… More >

Xiaofeng Liu^*, Yubin Bao, Fangling Leng

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3503-3517, 2025, DOI:10.32604/cmc.2025.064544 - 03 July 2025

Abstract The rapid development of evolutionary deep learning has led to the emergence of various Neural Architecture Search (NAS) algorithms designed to optimize neural network structures. However, these algorithms often face significant computational costs due to the time-consuming process of training neural networks and evaluating their performance. Traditional NAS approaches, which rely on exhaustive evaluations and large training datasets, are inefficient for solving complex image classification tasks within limited time frames. To address these challenges, this paper proposes a novel NAS algorithm that integrates a hierarchical evaluation strategy based on Surrogate models, specifically using supernet to… More >

Chunhuan Jin¹, Linsen Zhu², Quanliang Liu^1,3,*, Ji Lin¹

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1689-1711, 2025, DOI:10.32604/cmes.2025.063850 - 30 May 2025

Abstract This study proposes a multi-objective optimization framework for electric winches in fiber-reinforced plastic (FRP) fishing vessels to address critical limitations of conventional designs, including excessive weight, material inefficiency, and performance redundancy. By integrating surrogate modeling techniques with a multi-objective genetic algorithm (MOGA), we have developed a systematic approach that encompasses parametric modeling, finite element analysis under extreme operational conditions, and multi-fidelity performance evaluation. Through a 10-t electric winch case study, the methodology’s effectiveness is demonstrated via parametric characterization of structural integrity, stiffness behavior, and mass distribution. The comparative analysis identified optimal surrogate models for predicting More >

Bolin Zhang, Ruichun Gu^*, Haiying Liu

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2521-2535, 2025, DOI:10.32604/cmc.2024.060331 - 17 February 2025

Abstract Federated Graph Learning (FGL) enables model training without requiring each client to share local graph data, effectively breaking data silos by aggregating the training parameters from each terminal while safeguarding data privacy. Traditional FGL relies on a centralized server for model aggregation; however, this central server presents challenges such as a single point of failure and high communication overhead. Additionally, efficiently training a robust personalized local model for each client remains a significant objective in federated graph learning. To address these issues, we propose a decentralized Federated Graph Learning framework with efficient communication, termed Decentralized… More >

Ruoyan Li^1,2, Wenjing Ye^1,*, Yijun Liu²

The International Conference on Computational & Experimental Engineering and Sciences, Vol.32, No.1, pp. 1-1, 2024, DOI:10.32604/icces.2024.012150

Abstract A modern approach to control sound is through the development of sound-control materials/structures, which enable a wide range of applications such as noise reduction and non-contact particle manipulation. Designing these sound-controlling metamaterials requires accurate and efficient simulation methods for solving the unbounded acoustic wave equation with changing domain and frequencies. To facilitate the design optimization, surrogate models that are significantly more efficient than full-scale simulations are highly desirable. In this work, we present our recent work on the development of such surrogate models based on the concept of Fourier neural operators (FNO). FNO was originally… More >