Fatemeh Mobasheri¹, Masoud Hosseinpoor^1,*, Ammar Yahia^1,2, Farhad Pourkamali-Anaraki³

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3309-3347, 2025, DOI:10.32604/cmes.2025.072271 - 23 December 2025

Abstract Self-consolidating concrete (SCC) is an important innovation in concrete technology due to its superior properties. However, predicting its compressive strength remains challenging due to variability in its composition and uncertainties in prediction outcomes. This study combines machine learning (ML) models with conformal prediction (CP) to address these issues, offering prediction intervals that quantify uncertainty and reliability. A dataset of over 3000 samples with 17 input variables was used to train four ensemble methods, including Random Forest (RF), Gradient Boosting Regressor (GBR), Extreme gradient boosting (XGBoost), and light gradient boosting machine (LGBM), along with CP techniques, More >

Yingying Guo¹, Ziyu Cui², Jibing Shen¹, Pei Li^3,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4519-4550, 2025, DOI:10.32604/cmc.2025.071504 - 23 October 2025

Abstract Accurate simulation of acoustic wave propagation in complex structures is of great importance in engineering design, noise control, and related research areas. Although traditional numerical simulation methods can provide precise results, they often face high computational costs when applied to complex models or problems involving parameter uncertainties, particularly in the presence of multiple coupled parameters or intricate geometries. To address these challenges, this study proposes an efficient algorithm for simulating the acoustic field of structures with adhered sound-absorbing materials while accounting for ground reflection effects. The proposed method integrates Catmull-Clark subdivision surfaces with the boundary… More >

Shige Wang¹, Yalong Liang², Lian Huang³, Pei Li^4,*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2729-2748, 2025, DOI:10.32604/cmc.2025.068351 - 23 September 2025

Abstract This study introduces a hybrid Cuckoo Search-Deep Neural Network (CS-DNN) model for uncertainty quantification and composition optimization of Na_1/2Bi_1/2TiO₃ (NBT)-based dielectric energy storage ceramics. Addressing the limitations of traditional ferroelectric materials—such as hysteresis loss and low breakdown strength under high electric fields—we fabricate (1 − x)NBBT8-xBMT solid solutions via chemical modification and systematically investigate their temperature stability and composition-dependent energy storage performance through XRD, SEM, and electrical characterization. The key innovation lies in integrating the CS metaheuristic algorithm with a DNN, overcoming local minima in training and establishing a robust composition-property prediction framework. Our model accurately… 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 >

Anandhavalli Muniasamy^1,*, Ashwag Alasmari²

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 569-592, 2025, DOI:10.32604/cmes.2025.060484 - 11 April 2025

Abstract The effective and timely diagnosis and treatment of ocular diseases are key to the rapid recovery of patients. Today, the mass disease that needs attention in this context is cataracts. Although deep learning has significantly advanced the analysis of ocular disease images, there is a need for a probabilistic model to generate the distributions of potential outcomes and thus make decisions related to uncertainty quantification. Therefore, this study implements a Bayesian Convolutional Neural Networks (BCNN) model for predicting cataracts by assigning probability values to the predictions. It prepares convolutional neural network (CNN) and BCNN models. More > Graphic Abstract

Can Wu^1,2, Wenyi Tang², Yunbo Rao^1,2,*, Yinjie Chen¹, Hui Ding², Shuzhen Zhu³, Yuanyuan Wang³

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1415-1434, 2025, DOI:10.32604/cmc.2025.059797 - 26 March 2025

Abstract Infrared unmanned aerial vehicle (UAV) target detection presents significant challenges due to the interplay between small targets and complex backgrounds. Traditional methods, while effective in controlled environments, often fail in scenarios involving long-range targets, high noise levels, or intricate backgrounds, highlighting the need for more robust approaches. To address these challenges, we propose a novel three-stage UAV segmentation framework that leverages uncertainty quantification to enhance target saliency. This framework incorporates a Bayesian convolutional neural network capable of generating both segmentation maps and probabilistic uncertainty maps. By utilizing uncertainty predictions, our method refines segmentation outcomes, achieving… More >

Xinxin Yue¹, Jian Zhang^2,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.29, No.4, pp. 1-2, 2024, DOI:10.32604/icces.2024.011344

Abstract The "curse of dimensionality" faced by high-dimensional complex engineering problems can be tackled by a set of quantitative model evaluation and analysis tools named high-dimensional model representation (HDMR) [1,2], which has attracted much attention from researchers in various fields, such as global sensitivity analysis (GSA) [3], structural reliability analysis (SRA) [4], CFD uncertainty quantification [5] and so on [6]. In this paper, a new method for uncertainty quantification is proposed. Firstly, PCE-HDMR for SRA is developed by taking advantage of the accuracy and efficiency of PCE-HDMR for modeling high-dimensional problems [7]. Secondly, the formulas for… More >

Haojie Lian¹, Jiaqi Wang¹, Leilei Chen^2,*, Shengze Li³, Ruochen Cao⁴, Qingyuan Hu⁵, Peiyun Zhao¹

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 1143-1163, 2024, DOI:10.32604/cmes.2024.048549 - 16 April 2024

Abstract This paper presents a novel framework aimed at quantifying uncertainties associated with the 3D reconstruction of smoke from 2D images. This approach reconstructs color and density fields from 2D images using Neural Radiance Field (NeRF) and improves image quality using frequency regularization. The NeRF model is obtained via joint training of multiple artificial neural networks, whereby the expectation and standard deviation of density fields and RGB values can be evaluated for each pixel. In addition, customized physics-informed neural network (PINN) with residual blocks and two-layer activation functions are utilized to input the density fields of More >

Liang Zhang^1,*, Zigang He¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.27, No.2, pp. 1-1, 2023, DOI:10.32604/icces.2023.09713

Abstract The different elastic properties of tension and compression are obvious in many engineering materials, especially new materials. Materials with this characteristic, such as graphite, ceramics, and composite materials, are called bi-modulus materials. Their mechanical properties such as Young’s modulus have randomness in tension and compression due to different porosity, microstructure, etc. To calibrate the mechanical properties of bi-modulus materials by bridging FEM simulation results and scarce experimental data, the paper presents a data-fusion computational method. The FEM simulation is implemented based on Parametric Variational Principle (PVP), while the experimental result is obtained by Digital Image… More >

Yong Li^1,*, Joel P. Conte², Philip E. Gill³

CMES-Computer Modeling in Engineering & Sciences, Vol.120, No.3, pp. 517-543, 2019, DOI:10.32604/cmes.2019.06269

Abstract In the field of earthquake engineering, the advent of the performance-based design philosophy, together with the highly uncertain nature of earthquake ground excitations to structures, has brought probabilistic performance-based design to the forefront of seismic design. In order to design structures that explicitly satisfy probabilistic performance criteria, a probabilistic performance-based optimum seismic design (PPBOSD) framework is proposed in this paper by extending the state-of-the-art performance-based earthquake engineering (PBEE) methodology. PBEE is traditionally used for risk evaluation of existing or newly designed structural systems, thus referred to herein as forward PBEE analysis. In contrast, its use… More >