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  1. 1301

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

    Published 2025-07-01
    “…Abstract In the field of computational fluid dynamics, the accuracy of turbulence models is crucial. …”
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  2. 1302

    What are the parities of photon-ring images near a black hole? by Ashish Kumar Meena, Prasenjit Saha

    Published 2023-12-01
    “…In this work, we extend the notion of the time delay surface to strong gravitational fields and compute the surface for a Schwarzschild black hole. …”
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  3. 1303

    A modification design method for the angular misalignment of the spline drum shaping by YUAN Xichuan, LIU Qi, TANG Jinyuan, LI Yuan, SU Hui

    Published 2025-07-01
    “…The optimal modification design for <italic>P</italic><sub>max</sub> minimization was achieved by analyzing contact region features in the minimum-<italic>P</italic><sub>max</sub> sample and combining them with the established prediction formulas. …”
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    Coupling Strategies Investigation of Hybrid Atomistic-Continuum Method Based on State Variable Coupling by Qian Wang, Xiao-Guang Ren, Xin-Hai Xu, Chao Li, Hong-Yu Ji, Xue-Jun Yang

    Published 2017-01-01
    “…The major findings are as follows: (1) the C→A region plays the most important role in the overlap region and the “4-layer-1” combination achieves the best precision with a fixed width of the overlap region; (2) the data exchanging operation only needs a few sampling points closer to the occasions of interactions and decreasing the coupling exchange operations can reduce the computational load with acceptable errors; (3) the nonperiodic boundary force model with a smoothing parameter of 0.1 and a finer parameter of 20 can not only achieve the minimum disturbance near the MD-continuum interface but also keep the simulation precision.…”
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    Minimal‐cost route planning via Fibonacci‐heap‐typed data structure by Wangjia Zhan, Haohao Qiu, Bo Min, Lin Lin

    Published 2024-11-01
    “…Abstract Efficient route planning in transportation networks, particularly under stochastic conditions like severe weather (i.e. snow or hail), poses a significant computational challenge. This article addresses this challenge by modeling the route planning problem as a Markov decision process (MDP) problem, establishing reachability criteria, and identifying the minimum‐weight arborescence in the directed graph. …”
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