New adaptive meta-learning SGHMC algorithm enhances Bayesian updating for structural models

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

Researchers have developed a new adaptive meta-learning stochastic gradient Hamiltonian Monte Carlo (AM-SGHMC) algorithm designed to improve Bayesian updating of structural dynamic models. This method utilizes adaptive neural networks that can be applied to various structural updating problems without retraining, overcoming a significant limitation of previous approaches. The algorithm's effectiveness and generalization capabilities were demonstrated through Bayesian updating of multi-story building models. AI

影响 Introduces a more efficient and generalizable meta-learning approach for Bayesian updating in structural dynamics, potentially reducing computational costs.

排序理由 Academic paper introducing a novel algorithm for a specific application.

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

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

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

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

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

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

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…

报道来源 [3]

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

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

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

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