English(EN) Adaptive Meta-Learning Stochastic Gradient Hamiltonian Monte Carlo Simulation for Bayesian Updating of Structural Dynamic Models

新的自适应元学习SGHMC算法增强了结构模型的贝叶斯更新

作者 PulseAugur 编辑部 · [3 个来源] · 2026-04-28 14:34

研究人员开发了一种新的自适应元学习随机梯度哈密顿蒙特卡洛（AM-SGHMC）算法，旨在改进结构动力学模型的贝叶斯更新。该方法利用自适应神经网络，无需重新训练即可应用于各种结构更新问题，克服了先前方法的重大局限性。通过对多层建筑模型的贝叶斯更新证明了该算法的有效性和泛化能力。 AI

影响为结构动力学中的贝叶斯更新引入了一种更高效、更具泛化能力的元学习方法，有可能降低计算成本。

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Hugging Face Daily Papers TIER_1 English(EN) · 2026-04-28 14:34

面向结构动力学模型贝叶斯更新的自适应元学习随机梯度哈密顿蒙特卡洛模拟

In the last few decades, Markov chain Monte Carlo (MCMC) methods have been widely applied to Bayesian updating of structural dynamic models in the field of structural health monitoring. Recently, several MCMC algorithms have been developed that incorporate neural networks to enha…
arXiv stat.ML TIER_1 English(EN) · Xianghao Meng, James L. Beck, Yong Huang, Hui Li · 2026-04-29 04:00

用于结构动力学模型贝叶斯更新的自适应元学习随机梯度哈密顿蒙特卡洛模拟

arXiv:2604.25710v1 Announce Type: cross Abstract: In the last few decades, Markov chain Monte Carlo (MCMC) methods have been widely applied to Bayesian updating of structural dynamic models in the field of structural health monitoring. Recently, several MCMC algorithms have been …
arXiv stat.ML TIER_1 English(EN) · Hui Li · 2026-04-28 14:34

面向结构动力学模型贝叶斯更新的自适应元学习随机梯度哈密顿蒙特卡洛模拟

In the last few decades, Markov chain Monte Carlo (MCMC) methods have been widely applied to Bayesian updating of structural dynamic models in the field of structural health monitoring. Recently, several MCMC algorithms have been developed that incorporate neural networks to enha…