English(EN) MoRF-AST: Calibrated Probabilistic Virtual Sensing for Structural Monitoring under Changing Operating Conditions

新的MoRF-AST框架为结构监测校准AI不确定性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-26 04:00

研究人员开发了MoRF-AST，一个用于结构监测的校准概率虚拟传感的新型框架。该方法解决了在运行条件偏离训练数据时保持准确不确定性估计的挑战。MoRF-AST构建了一个高斯参考后验，并使用在白化残差上训练的条件流。然后，一个仿射扩散传输（AST）组件利用历史测量值调整后验扩散，显著减少了跨域覆盖误差，同时保持了准确性。该方法旨在为土木和基础设施工程提供值得信赖的概率建模。 AI

影响通过确保即使在运行条件变化的情况下，不确定性估计也能保持准确，从而提高了AI驱动的结构监测的可靠性。

排序理由该集群包含一篇详细介绍概率建模新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Wingho Feng, Quanwang Li, Ming Zhong, Jingyu Yang, Chen Wang · 2026-08-26 04:00

MoRF-AST：在运行条件变化下用于结构监测的校准概率虚拟传感

arXiv:2608.24531v1 Announce Type: cross Abstract: Probabilistic full-field reconstruction provides uncertainty-aware response evidence for structural reliability assessment, yet inference from sparse and noisy measurements remains underdetermined. Most existing methods overlook s…

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MoRF-AST：在运行条件变化下用于结构监测的校准概率虚拟传感

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