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English(EN) A Bayesian latent Gaussian process framework for aerodynamic uncertainty quantification

新的贝叶斯框架增强了气动不确定性量化

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种新的贝叶斯潜在高斯过程框架，以提高气动不确定性量化的准确性。该方法使用稀疏的实验测量来校准低保真计算模型，而这些测量本身也存在不确定性。该框架有效地对输入不确定性进行边际化，并匹配输出不确定性的均值和方差，即使在推断场景中也能对气动系数进行高度准确的预测。验证表明，该模型的预测具有高保真度，落在真实不确定性区间内。 AI

影响 该框架通过更好地考虑不确定性，可以提高航空航天工程等领域的模拟可靠性。

排序理由 该集群包含一篇详细介绍新方法的学术论文。

在 arXiv stat.ML 阅读 →

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新的贝叶斯框架增强了气动不确定性量化

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报道来源 [2]

  1. arXiv stat.ML TIER_1 English(EN) · Geoffrey Davis, Ashwin Renganathan ·

    一种用于气动不确定性量化的贝叶斯潜在高斯过程框架

    arXiv:2606.28871v1 Announce Type: new Abstract: Predicting the aerodynamic performance (e.g. lift, drag, and moment coefficients) of an aircraft is challenging -- computational models are biased and direct simulations are prohibitive. A pragmatic way to overcome this limitation i…

  2. arXiv stat.ML TIER_1 English(EN) · Ashwin Renganathan ·

    一种用于气动不确定性量化的贝叶斯潜在高斯过程框架

    Predicting the aerodynamic performance (e.g. lift, drag, and moment coefficients) of an aircraft is challenging -- computational models are biased and direct simulations are prohibitive. A pragmatic way to overcome this limitation is by calibrating low-fidelity computational pred…

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