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English(EN) Conformal risk control for model-form uncertainty in parametric non-intrusive reduced-order models

新框架增强降阶模型中的不确定性量化

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新的框架，用于量化非侵入式降阶模型（NIROMs）中的不确定性。该方法将降阶基的随机表示与共形风险控制技术相结合，以提供可靠的预测集。该方法在不重新训练高斯过程的情况下，将基引起的（basis-induced）不确定性与回归引起的（regression-induced）不确定性分开，从而更准确地评估模型可靠性，尤其是在外推场景中。 AI

影响 增强了计算模型的可靠性，可能提高了AI驱动的设计和模拟精度。

排序理由 该集群包含一篇学术论文，详细介绍了计算建模中不确定性量化的一种新方法。[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 →

新框架增强降阶模型中的不确定性量化

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该集群包含一篇学术论文，详细介绍了计算建模中不确定性量化的一种新方法。[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) · Edgar Jaber (CB, ENS Paris Saclay), R\'emy Vallot (CB, Michelin), Thibault Dairay (CB, Michelin), Mathilde Mougeot (CB, ENSIIE, ENS Paris Saclay) ·

    参数化非侵入式降阶模型中模型形式不确定性的保形风险控制

    arXiv:2608.03360v1 Announce Type: new Abstract: Non-intrusive reduced-order models (NIROMs) have become a standard tool for approximating parametric partial differential equations from computer design of experiments while significantly reducing computational costs. However, asses…

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