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English(EN) Dual-domain fused LSTM modeling for efficient time-dependent reliability analysis

新型DDF-LSTM模型增强了时变可靠性分析

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新的双域融合长短期记忆（DDF-LSTM）模型，以提高工程系统时变可靠性分析的准确性和效率。该模型独特地将与时间无关的变量集成到初始隐藏状态中，并使用全连接层将LSTM输出与这些变量结合起来。增强的损失函数侧重于模型对最小响应的敏感性，从而更精确地估计失效概率。DDF-LSTM模型能有效捕捉变量、随机过程和极限状态函数时间行为之间的复杂依赖关系，从而能够进行快速的蒙特卡洛模拟以进行可靠性评估。 AI

影响 这项研究为工程领域改进可靠性分析引入了一种新颖的深度学习架构，有望带来更强大、更高效的系统设计。

排序理由 介绍新颖模型架构的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新型DDF-LSTM模型增强了时变可靠性分析

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介绍新颖模型架构的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Yixin Zhang, Mingyang Li, Zichao Jiang ·

    用于高效时变可靠性分析的双域融合LSTM建模

    arXiv:2607.18291v1 Announce Type: cross Abstract: Time-dependent reliability analysis is crucial for ensuring the long-term safety and performance of engineering systems under uncertainties. However, traditional surrogate model methods often struggle to incorporate time-independe…

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