A fast physics-based perturbation generator of machine learning weather model for efficient ensemble forecasts of tropical cyclone track by Jingchen Pu, Mu Mu, Jie Feng, Xiaohui Zhong, Hao Li

“…Although emerging artificial intelligence (AI)-based weather models offer high forecast accuracy and improved computational efficiency, they still face considerable challenges in ensemble forecasting applications, due to the unclear error growth dynamic and the lack of suitable ensemble methods in AI-based models. …”
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Mathematical Methods Applied to the Problem of Dairy Cow Replacements: A Scoping Review by Osvaldo Palma, Lluis M. Plà-Aragonés, Alejandro Mac Cawley, Víctor M. Albornoz

“…This research underscores the importance of these methods in improving the efficiency, profitability, and sustainability of dairy farming operations. …”
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Deep Reinforcement Learning for RIS-Assisted Multi-UAV MU-MISO Communication Networks: Sum-Rate and Energy Efficiency Maximization by Alif Rahmatullah Umar, Hasan Albinsaid, Chia-Po Wei, Chih-Peng Li

“…We formulate separate optimization problems for sum-rate and energy efficiency, and address them using deep reinforcement learning (DRL) algorithms, namely proximal policy optimization (PPO) and deep deterministic policy gradient (DDPG). …”
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Benchmarking the Non-Paraxial Beam Propagation Method for Deep-Tissue Microscopy by Praveen Kalarickel Ramakrishnan, Qi Hu, Peter R. T. Munro

“…This study evaluates the accuracy and computational efficiency of the Beam Propagation Method (BPM) for modeling light propagation in microscopy of biological tissue. …”
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A multiobjective continuation method to compute the regularization path of deep neural networks by Augustina Chidinma Amakor, Konstantin Sonntag, Sebastian Peitz

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An Automatic Damage Detection Method Based on Adaptive Theory-Assisted Reinforcement Learning by Chengwen Zhang, Qing Chun, Yijie Lin

“…Current damage detection methods based on model updating and sensitivity Jacobian matrixes show a low convergence ratio and computational efficiency for online calculations. …”
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Advances in UAV Path Planning: A Comprehensive Review of Methods, Challenges, and Future Directions by Wenlong Meng, Xuegang Zhang, Lvzhuoyu Zhou, Hangyu Guo, Xin Hu

“…This article presents an extensive overview of methodologies for UAV route planning, including deterministic models, stochastic sampling techniques, biologically inspired methods, and integrated algorithmic frameworks. …”
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An Optimized Path Planning Method for Coastal Ships Based on Improved DDPG and DP by Yiquan Du, Xiuguo Zhang, Zhiying Cao, Shaobo Wang, Jiacheng Liang, Fengge Zhang, Jiawei Tang

“…In this paper, an optimized path planning method for coastal ships based on improved Deep Deterministic Policy Gradient (DDPG) and Douglas–Peucker (DP) algorithm is proposed. …”
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Efficient IP address retrieval using a novel octet based encoding technique for high speed lookup to improve network performance by Veeramani Sonai, Indira Bharathi, Sajjad Shaukat Jamal, Zaid Bassfar, Sameer Abdullah Nooh

“…This paper proposes an efficient deterministic approach to encoding IP prefixes that reduces storage complexity. …”
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Hybridization of stochastic hydrological models and machine learning methods for improving rainfall-runoff modeling by Sianou Ezéckiel Houénafa, Olatunji Johnson, Erick K. Ronoh, Stephen E. Moore

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A Novel Method Based on Particle Flow Filters for Stellar Gyroscope Parameter Estimations by Erol Duymaz

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Uncertainty modeling of excavator’s variable displacement pump based on EFAST and MH-Cuckoo method by Su Deying, Lian Xiaozhen, Xie Meina

“…The uncertainty method yielded average MARE values of 1.24 %, 1.92 %, 2.57 %, and 3.02 %, outperforming the deterministic method (2.74 %, 3.41 %, 5.30 %, and 7.20 %). …”
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Electromagnetic Scattering of Electrically Large Ship above Sea Surface with SBR-SDFM Method by Lixin Guo, Tiantian Feng

“…Accuracy and performance of this method are validated and evaluated by comparing with multilevel fast multipole method of FEKO for electrically small objects. …”
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Research on Random Discrete Ordinates Method Based on Quasi-Monte Carlo Integration Technique by DAI Tao, XU Longfei, LI Baiwen, SHEN Huayun, HU Yuan, MA Ruiyao

“…Owing to its superior computational accuracy and efficiency compared to alternative numerical approaches, the SN method has been extensively adopted in neutron transport applications, particularly in nuclear reactor design and radiation shielding optimization. …”

Distributionally Robust Policy and Lyapunov-Certificate Learning by Kehan Long, Jorge Cortes, Nikolay Atanasov

“…This article presents novel methods for synthesizing distributionally robust stabilizing neural controllers and certificates for control systems under model uncertainty. …”
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Development of Hybrid Neutron Dynamics Algorithm Based on Transient Fission Matrix Combined Method by LI Yuehang, HE Donghao, LIU Xiaojing, PAN Qingquan

“…The deterministic method and the Monte Carlo method are used in the transient analysis of nuclear reaction systems, but high-fidelity methods are inefficient in computation, and the accuracy of the low order method is not sufficient. …”

Research on Parallel Computing Method for Neutron Transport on Tetrahedral Unstructured Meshes Based on COME by ZHONG Yangyu, ZHANG Tiancheng, ZHOU Xiafeng

“…Owing to the inhomogeneous distribution of materials in complex geometric reactor cores, traditional source iteration methods face significant challenges in solving neutron transport equations efficiently. …”

A Tactical Conflict Detection and Resolution Method for En Route Conflicts in Trajectory-Based Operations by Dong Sui, Kai Zhang

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FedDDPG: A reinforcement learning method for federated learning-based vehicle trajectory prediction by Jinlong Li, Ruonan Li, Guojie Ma, Weihong Yang, Hongye Wang, Zhaoquan Gu

“…However, trajectory data collected from roadside units often contains varying levels of noise, which poses unique challenges for traditional FL methods. To address these challenges, this paper proposes a personalized optimization solution called FedDDPG (Federated Learning with Deep Deterministic Policy Gradient) for VTP with FL paradigm. …”
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Decision-Making Policy for Autonomous Vehicles on Highways Using Deep Reinforcement Learning (DRL) Method by Ali Rizehvandi, Shahram Azadi, Arno Eichberger

“…Automated driving (AD) is a new technology that aims to mitigate traffic accidents and enhance driving efficiency. This study presents a deep reinforcement learning (DRL) method for autonomous vehicles that can safely and efficiently handle highway overtaking scenarios. …”
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