AM-SGHMC combines adaptive neural networks with SGHMC to produce a reusable MCMC sampler for Bayesian updating of similar structural dynamic models without per-task retraining.
Bayesian updating of structural models and reliability using Markov chain Monte Carlo simulation
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APM-SGHMC achieves zero-shot generalization in MCMC sampling for Bayesian system identification by adaptively aligning with principal components to enforce translation, scale, and rotation invariance.
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Adaptive Meta-Learning Stochastic Gradient Hamiltonian Monte Carlo Simulation for Bayesian Updating of Structural Dynamic Models
AM-SGHMC combines adaptive neural networks with SGHMC to produce a reusable MCMC sampler for Bayesian updating of similar structural dynamic models without per-task retraining.
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MCMC with Adaptive Principal-Component Transformation: Rotation-Invariant Universal Samplers for Bayesian Structural System Identification
APM-SGHMC achieves zero-shot generalization in MCMC sampling for Bayesian system identification by adaptively aligning with principal components to enforce translation, scale, and rotation invariance.