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English(EN) Probabilistic function-on-function nonlinear autoregressive model for emulation and reliability analysis of stochastic dynamical systems

新的F2NARX模型为动力学系统提供了显著的效率和准确性提升

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出了一种新的函数-函数非线性自回归模型（F2NARX），它提高了复杂动力学系统的预测效率和准确性。这一新颖的框架将主成分分析与高斯过程回归相结合，通过自回归方式的无迹变换实现概率预测。F2NARX模型在速度和精度上都显著优于现有的NARX模型，并且其主动学习能力能够以最少的数据量准确估计首次通过失效概率。 AI

影响 这项新的建模技术可以提高工程和科学研究中模拟的效率和准确性。

排序理由 该集群包含一篇详细介绍新颖建模技术的新学术论文。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv stat.ML 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的F2NARX模型为动力学系统提供了显著的效率和准确性提升

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该集群包含一篇详细介绍新颖建模技术的新学术论文。[lever_c_demoted from research: ic=1 ai=0.7]
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Zhouzhou Song, Marcos A. Valdebenito, Styfen Sch\"ar, Stefano Marelli, Bruno Sudret, Matthias G. R. Faes ·

    随机动力学系统的仿真与可靠性分析的概率函数-函数非线性自回归模型

    arXiv:2602.01929v2 Announce Type: replace-cross Abstract: Constructing accurate and computationally efficient surrogate models (or emulators) for predicting dynamical system responses is critical in many engineering domains, yet remains challenging due to the strongly nonlinear a…

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