Efficient Finite Element Methodology Based on Cartesian Grids: Application to Structural Shape Optimization by E. Nadal, J. J. Ródenas, J. Albelda, M. Tur, J. E. Tarancón, F. J. Fuenmayor

“…The cg-FEM methodology uses advanced recovery techniques to obtain an improved solution of the displacement and stress fields (for which a discretization error estimator in energy norm is available) that will be the output of the analysis. …”
Get full text

Age and initial spherical equivalent influence the axial length elongation in children aged 9–17 years undergoing orthokeratology therapy by Guangbin Zhong, Ruinian Zheng, Linyi Luo

Get full text

Converging efficiency: Computational and fractal insights into parallel non-linear schemes by Mudassir Shams, Nasreen Kausar, Ali Akgül, Tonguç Çağın

“…Randomly chosen initial values are employed to demonstrate the engineering problems have been subjected to comparative analysis, which shows that the suggested parallel schemes surpass traditional methods in residual error, convergence rate, CPU time, memory usage, and computational cost. …”
Get full text

Optimization of Gas Dynamical Subsystem of Transducers for Measurement of Gas Flow Temperature by Vasyl Fedynets, Ihor Vasylkivskyi, Yaroslav Yusyk

Get full text

Effect of Flexible 3D Multipoint Stretch Bending Dies on the Shape Accuracy and the Optimal Design by Xiangfeng Lin, Yi Li, Zhongyi Cai, Jicai Liang, Ce Liang, Kaifeng Yu, Xiaoming Li, Aicheng Wang, Yanfei Liao

Get full text

Dimensionality Reduction Optimization of High Subsonic Unmanned Aerial Vehicles Intake by Zeqi QI, Zheng GUO, Suqi CHEN, Ke YU

“…The results show that after dimensionality reduction through sensitivity analysis and PCA, the average error and coefficient of determination of the surrogate model were improved. …”
Get full text

Improving prediction of solar radiation using Cheetah Optimizer and Random Forest. by Ibrahim Al-Shourbaji, Pramod H Kachare, Abdoh Jabbari, Raimund Kirner, Digambar Puri, Mostafa Mehanawi, Abdalla Alameen

“…Evaluation metrics encompassing Mean Absolute Error (MAE), Mean Squared Error (MSE), and coefficient of determination (R2) are employed to validate its performance. …”
Get full text

Optimizing concrete strength: How nanomaterials and AI redefine mix design by Dan Huang, Guangshuai Han, Ziyang Tang

“…However, the complex interactions between nanomaterials, SCMs, and cement make concrete mix design a challenging, iterative, and labor-intensive process, often relying on trial-and-error experimentation. Machine learning (ML) offers an opportunity to better understand the influence of input parameters and to accelerate the optimization of mix designs through data-driven insights. …”
Get full text

Optimization and Experimental Testing of the Drag Torque Model Based on the Cavitation Effect by Li Jie, Lian Hailong, Lan Hai, Wang Zhiyong

“…Then, the wet brake drag torque is tested. After comparative analysis, the results show that the prediction error of the optimized model is less than 6%, and the average error is reduced by 55% compared with the model without the cavitation effect. …”
Get full text

Purchasing Prediction Using Machine Learning Algorithms for Optimizing Inventory Management by Reza Hamdi Prayetno, Rani Destika Purba, Kyrene Wirawan, Kelvin Sweet, Evta Indra

“…By analyzing sales data for 2023 which consists of 96,630 lines, the research applies systematic stages: data acquisition, preprocessing, exploratory data analysis, model building, and evaluation. The LSTM method is used to predict spare parts stock with significant accuracy, demonstrated through evaluation metrics: Mean Absolute Error (MAE) 12%, Mean Squared Error (MSE) 2%, and Root Mean Square Error (RMSE) 15%. …”
Get full text

Optimal receiver and blind demodulation performance bounds for single channel co-frequency mixed signals by YU Hongyi, ZHA Renpeng, SHEN Zhixiang, SHEN Caiyao, HU Yunpeng

“…The result shows that the simulation results of the optimal receiver coincide with the theoretical performance analysis results, verifying the reasonableness of the derived performance bounds.…”
Get full text

Parameter Optimization Method for Predictor–Corrector Guidance With Impact Angle Constraint by Xinwan Kong, Cheng Zhang

“…Currently, the key parameters of predictor–corrector guidance law often rely on the designer’s experience and trial-and-error selection. It requires a significant amount of time for comprehensive testing to verify its reliability, lacking a theoretical basis for stability and robustness analysis. …”
Get full text

Optimization of Biaxial Tensile Specimen Shapes on Aerospace Composite with Large Deformation by Haowen Luo, Jiangtao Wang, Xueren Wang, Xiangyang Liu

“…This configuration demonstrated excellent performance stability during deformation, with final stress uniformity error controlled to within 2.2%. The final strain uniformity error was maintained at 2.9%. …”
Get full text

Cement Slurry Plugging Law and Optimal Plugging Flow Rate at a High Hydraulic Gradient by Peili Su, Yifei Jia, Feng Liu, Chong Li

“…Through a comparative analysis of the experimental and theoretical values, the optimal plugging velocity of pure cement slurry was 0.5–0.55 m·s−1 under different conditions, and the error between the experimental and theoretical values was less than 0.1 m·s−1, which confirmed the rationality of the proposed model.…”
Get full text

Composite Learning-Based Inverse Optimal Fault-Tolerant Control for Hierarchy-Structured Unmanned Helicopters by Qingyi Liu, Ke Zhang, Bin Jiang, Yushun Tan

“…Next, the serial-parallel estimation model, designed to account for prediction error, is developed. On this foundation, the composite updating law for network weights is derived. …”
Get full text

Application of deep reinforcement learning in parameter optimization and refinement of turbulence models by Zhan Zhang

“…The DDPG optimization method significantly reduced the MAE (Mean Absolute Error) and RMSE (Root Mean Square Error) of the WPC, and its optimization effect was significantly better than the GA (Genetic Algorithm) and PSO (Particle Swarm Optimization) methods.…”
Get full text

Calibration and optimization of discrete element method parameter of Pinus koraiensis seeds by Chun Wang, Zhizheng Hu, Xiangyu Liu, Hailin Kui, Yongchao Shao

“…The minimal relative error between simulated and experimental repose angles was 1.52%. …”
Get full text

Optimal Adaptive Robust Pitch Control with Load Mitigation for Uncertain Variable Speed Wind Turbines by Sara Majidi, Reza Shahnazi

“…In this paper, an optimal adaptive robust pitch controller is proposed for variable speed wind turbines (VSWTs). …”
Get full text

Gear Whine Optimization Design of Diesel Engine Timing Gear System by Zeng Xiaochun, Zhao Zhisheng, Wang Yi, Wei Tao, Huang Xiupeng, Zheng Guangze

“…Based on the analytical model, the transmission error, contact pattern and vibration response are investigated. …”
Get full text

Calculating the Optimal Point Cloud Density for Airborne LiDAR Landslide Investigation: An Adaptive Approach by Zeyuan Liao, Xiujun Dong, Qiulin He

“…After analysis of DEM quality at eight point cloud dilution levels, the optimal ground point cloud densities were determined to be 2.43 pts/m² (0.2 m resolution), 2.08 pts/m² (1 m and 0.5 m resolution), and 1.84 pts/m² (2 m resolution). …”
Get full text