English(EN) Active learning for data-driven reduced models of parametric differential systems with Bayesian operator inference

新的主动学习框架通过贝叶斯算子推断增强降阶模型

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-04 04:00

研究人员开发了一个新的主动学习框架，旨在改进参数化动力系统的面向数据的降阶模型（ROM）。该框架使用贝叶斯算子推断（将其构建为贝叶斯线性回归）来创建概率性ROM。通过分析预测不确定性，系统自适应地选择新的训练参数来增强ROM的稳定性和准确性，在偏微分方程的数值实验中优于随机采样。 AI

影响通过提高降阶模型训练的效率，这项研究可能带来更准确、更稳定的数字孪生和模拟。

排序理由该条目是一篇学术论文，详细介绍了一种新的科学机器学习方法。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv stat.ML TIER_1 English(EN) · Shane A. McQuarrie, Mengwu Guo, Anirban Chaudhuri · 2026-09-04 04:00

面向参数化微分系统的面向数据的降阶模型的激活学习与贝叶斯算子推断

arXiv:2601.00038v2 Announce Type: replace Abstract: This work develops an active learning framework to intelligently enrich data-driven reduced-order models (ROMs) of parametric dynamical systems, which can serve as the foundation of virtual assets in a digital twin. Data-driven …