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English(EN) Diffusion-Based Stochastic Operator Networks for Uncertainty Quantification in Stochastic Partial Differential Equations

新的SON框架量化SPDE中的不确定性

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一个名为随机算子网络（SON）的新框架，用于量化随机偏微分方程（SPDEs）中的不确定性。该方法结合了深度算子网络和随机神经网络，直接从噪声数据中学习，提供均值解和不确定性量化。在基准SPDEs上的实验表明，SON在捕捉解结构和预测不确定性方面是有效的。 AI

影响 引入了一种新颖的方法来提高复杂物理系统中使用的模型的可靠性。

排序理由 发表了一篇学术论文，详细介绍了在SPDEs中进行不确定性量化的一种新方法。

在 arXiv stat.ML 阅读 →

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新的SON框架量化SPDE中的不确定性

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报道来源 [2]

  1. arXiv stat.ML TIER_1 English(EN) · Phuoc-Toan Huynh, Richard Archibald, Feng Bao ·

    用于随机偏微分方程中不确定性量化的基于扩散的随机算子网络

    arXiv:2605.17107v1 Announce Type: new Abstract: We introduce a novel framework for uncertainty quantification of solution operators associated with stochastic partial differential equations (SPDEs). Although SPDEs play a central role in modeling complex physical systems under unc…

  2. arXiv stat.ML TIER_1 English(EN) · Feng Bao ·

    用于随机偏微分方程中不确定性量化的基于扩散的随机算子网络

    We introduce a novel framework for uncertainty quantification of solution operators associated with stochastic partial differential equations (SPDEs). Although SPDEs play a central role in modeling complex physical systems under uncertainty, their practical use typically requires…

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