STRUCTURAL RELIABILITY OPTIMIZATION DESIGN OF REINFORCED SHELL MODEL BASED ON ADAPTIVE SURROGATE MODEL by LIU YuZhuo, CAO LiXiong, WU JianGuo, LI HaiBo

“…Reinforced shell structure is widely used in aerospace load-bearing structures because its high specific stiffness and specific strength.By considering the uncertainty and risk factors in the structural parameters，the Reliability-Based Design Optimization （RBDO） can avoid the overly conservative design of the structure and ensure its reliability and safety.An efficient RBDO method based on adaptive agent model was proposed.This method solves the problem of lightweight design of reinforced shell structure under buckling reliability constraints.The adaptive addition of sample points was implemented through the expected feasibility function criterion，and the discrete variables was continued by constructing piecewise functions.This increases optimization efficiency while ensuring the reliability of design results.Finally，the effectiveness of the proposed method is verified by comparing the RBDO results with the deterministic optimization results.…”
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STRUCTURAL RELIABILITY OPTIMIZATION DESIGN OF REINFORCED SHELL STRUCTURE BASED ON ADAPTIVE SURROGATE MODEL by LIU Yuzhuo, CAO Lixiong, WU Jianguo, LI Haibo

“…Reinforced shell structure is widely used in aerospace load-bearing structures because its high specific stiffness and specific strength.By considering the uncertainty and risk factors in the structural parameters, the reliability-based design optimization (RBDO) can avoid the overly conservative design of the structure and ensure its reliability and safety.An efficient RBDO method based on adaptive surrogate model was proposed to solve the problem of lightweight design of reinforced shell structure under buckling reliability constraints.The adaptive addition of sample points was implemented through the expected feasibility function criterion, and the discrete variables was continued by constructing piecewise functions.This increases optimization efficiency while ensuring the reliability of design results.Finally, the effectiveness of the proposed method is verified by comparing the RBDO results with the deterministic optimization results.…”
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Multi-Energy Microgrid Data-Driven Distributionally Robust Optimization Dispatch Considering Uncertainty Correlation by LI Jiawei, SUN Qinghe, WANG Qiong, YE Yujian, HU Heng, ZHANG Xi

“…[Conclusions] The proposed method enhances the scheduling efficiency and ensures reliability，which collectively demonstrates its superiority.…”
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A novel explicit scheme for stochastic diffusive SIS models with treatment effects by Muhammad Shoaib Arif

“…The scheme is designed as an explicit two-stage method, where only the time-dependent terms are discretized, ensuring computational efficiency. …”
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Uncertainty-Aware Multimodal Trajectory Prediction via a Single Inference from a Single Model by Ho Suk, Shiho Kim

“…Our approach employs deterministic single forward pass methods, optimizing computational efficiency while retaining robust prediction accuracy. …”
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Non-Stationary Random Vibration of FE Structures Subjected to Moving Loads by F. Lu, D. Kennedy, F.W. Williams, J.H. Lin

“…An efficient and accurate FEM based method is proposed for studying non-stationary random vibration of structures subjected to moving loads. …”
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System of Interconnected Reactor Models of the Sulfur Recovery Unit with Hydrogen Extraction for Hydrogen Energy in a Fuzzy Environment by Batyr Orazbayev, Kulman Orazbayeva, Kanagat Dyussekeyev, Tursinbay Turymbetov, Ramazan Yessirkessinov, Ainur Zhumadillayeva

“…This study proposes a systematic modeling approach that integrates deterministic, statistical, and fuzzy logic methods to enhance process efficiency and accuracy. …”
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Optimizing resource allocation in industrial IoT with federated machine learning and edge computing integration by Ala'a R. Al-Shamasneh, Faten Khalid Karim, Yu Wang

“…The method also achieved a 40.5% improvement in computational efficiency and a 30-50% reduction in system costs, demonstrating its practicality and scalability. …”
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A Novel Dynamic Lane-Changing Trajectory Planning Model for Automated Vehicles Based on Reinforcement Learning by Cenxin Yu, Anning Ni, Jing Luo, Jinghui Wang, Chunqin Zhang, Qinqin Chen, Yifeng Tu

“…This study develops a lane-changing model using the deep deterministic policy gradient method, which can simultaneously control the lateral and longitudinal motions of the vehicle. …”
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On the accurate computation of expected modularity in probabilistic networks by Xin Shen, Matteo Magnani, Christian Rohner, Fiona Skerman

“…In this paper, we implement and compare our method and various general approaches for expected modularity computation in probabilistic networks. …”
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A Proposed Stochastic Finite Difference Approach Based on Homogenous Chaos Expansion by O. H. Galal

“…Two well-known equations were used for efficiency validation of the method proposed. First one being the linear diffusion equation with stochastic parameter and the second is the nonlinear Burger's equation with stochastic parameter and stochastic initial and boundary conditions. …”
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UAV spatiotemporal crowdsourcing resource allocation based on deep reinforcement learning by Yaxi LIU, Xulong LI, Jiahao HUO, Wei HUANGFU

“…To solve this MDP, we employ the soft actor critic (SAC) algorithm, an advanced deep reinforcement learning method known for its sample efficiency and stability. …”
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Rapid Probabilistic Inundation Mapping Using Local Thresholds and Sentinel-1 SAR Data on Google Earth Engine by Jiayong Liang, Desheng Liu, Lihan Feng, Kangning Huang

“…Traditional inundation mapping often relies on deterministic methods that offer only binary outcomes (inundated or not) based on satellite imagery analysis. …”
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Multiscale Sieve for Smart Prime Generation and Application in Info-Security, IoT and Blockchain by Gerardo Iovane, Elmo Benedetto, Carmine Gallo

“…The huge computational cost required to test whether a number is prime and the inefficiency of the known sieving algorithms for extremely large inputs have posed significant challenges in computational number theory. Traditional deterministic prime generation methods struggle to maintain performance when the input sizes increase exponentially. …”
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Stochastic carbon footprint tracing for power systems with uncertainty by Jiashuo Hu, Mengge Shi, Xiao‐ping Zhang, Youwei Jia

“…Recognizing that the CEF network complexity increases with higher DER penetration, the second method extends the initial approach to enhance computational efficiency while maintaining accuracy, thus ensuring scalability for large‐scale power system topologies. …”
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Local and global sensitivity analysis of key durability parameters of concrete under chloride environment by Lingjie Wu, Chengjian Yu, Fenfei Shi, Xuping Ni

“…By integrating the One-At-a-Time (OAT) method for local sensitivity analysis with the extended Fourier Amplitude Sensitivity Test (EFAST) and Sobol methods for global sensitivity analysis, a systematic evaluation is conducted to identify distinct influence mechanisms of these parameters under deterministic and time-dependent durability life prediction models. …”
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Earthquake Decision-Making Tool for Humanitarian Logistics Network: An Application in Popayan, Colombia by Helmer Paz-Orozco, Irineu de Brito Junior, Mario Chong, Yesid Anacona-Mopan, Jhon Alexander Segura Dorado, Mariana Moyano

“…<i>Background</i>: This study presents a comprehensive methodology for enhancing humanitarian logistics planning and management in natural disasters, focusing on earthquakes. <i>Methods</i>: The innovative approach combines a deterministic mathematical model with a simulation model to address the problem from multiple perspectives, aiming to improve efficiency and equity in post-disaster supply distribution. …”
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Coupling Machine Learning and Physically Based Hydrological Models for Reservoir-Based Streamflow Forecasting by Benjun Jia, Wei Fang

“…Taking the Yalong River as an example, the main results are as follows: (1) Deep learning models (ConvLSTM and LSTM) show good performance in forecast precipitation correction and reservoir operation rule extraction, contributing to streamflow forecasting accuracy. (2) The proposed streamflow deterministic forecasting method has good forecasting performance with <i>NSE</i> above 0.83 for the following 1–5 days. (3) The GMM model, using upstream evolutionary forecasted streamflow, interval forecasted streamflow, and downstream forecasted streamflow as the input–output combination, has good probabilistic forecasting performance and can adequately characterize the “non-normality” and “heteroskedasticity” of forecasting uncertainty.…”
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Determinate arbitrary quantum state engineering through one-dimensional quantum walks by Yu Wang, Meng Li, Yuan-Yuan Zhao

“…This efficient method for preparing quantum superposition states lays a foundation for advancing quantum technologies, with broad applications in quantum information processing.…”
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Fundamental properties and characteristics of flux distribution tallies using proper orthogonal decomposition by Kondo Ryoichi, Yamamoto Akio, Endo Tomohiro

“…This result indicates that the deterministic method might be more efficient for the snapshot calculation. …”
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