Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends | Paper Citation Record

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This paper cites Novel sparseness-inducing dual Kalman filter and its application to tracking time-varying spatially-sparse structural stiffness changes and inputs.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Novel sparseness-inducing dual Kalman filter and its application to tracking time-varying spatially-sparse structural stiffness changes and inputs

Reference 92

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Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Unresolved cited work

Reference 110

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Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Unresolved cited work

Reference 135

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This paper cites A hybrid optimization algorithm with Bayesian inference for probabilistic model updating.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends A hybrid optimization algorithm with Bayesian inference for probabilistic model updating

Reference 153

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This paper cites Structural health monitoring and fatigue damage estimation using vibration measurements and finite element model updating.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Structural health monitoring and fatigue damage estimation using vibration measurements and finite element model updating

Reference 154

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This paper cites Learning functional priors and posteriors from data and physics.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Learning functional priors and posteriors from data and physics

Reference 155

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This paper cites Model updating using noisy response measurements without knowledge of the input spectrum.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Model updating using noisy response measurements without knowledge of the input spectrum

Reference 156

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This paper cites On prediction error correlation in Bayesian model updating.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends On prediction error correlation in Bayesian model updating

Reference 157

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Reference 158

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This paper cites Bayesian system identification for structures considering spatial and temporal correlation.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Bayesian system identification for structures considering spatial and temporal correlation

Reference 159

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This paper cites A new adaptive importance sampling scheme for reliability calculations.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends A new adaptive importance sampling scheme for reliability calculations

Reference 160

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This paper cites Bayesian model updating of a coupled-slab system using field test data utilizing an enhanced Markov chain Monte Carlo simulation algorithm.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Bayesian model updating of a coupled-slab system using field test data utilizing an enhanced Markov chain Monte Carlo simulation algorithm

Reference 161

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This paper cites DRAM: Efficient adaptive MCMC.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends DRAM: Efficient adaptive MCMC

Reference 162

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This paper cites A new Gibbs sampling based algorithm for Bayesian model updating with incomplete complex modal data.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends A new Gibbs sampling based algorithm for Bayesian model updating with incomplete complex modal data

Reference 163

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This paper cites Transitional Markov chain Monte Carlo: observations and improvements.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Transitional Markov chain Monte Carlo: observations and improvements

Reference 164

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This paper cites Bayesian annealed sequential importance sampling (BASIS): an unbiased version of transitional Markov chain Monte Carlo.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Bayesian annealed sequential importance sampling (BASIS): an unbiased version of transitional Markov chain Monte Carlo

Reference 165

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This paper cites X -TMCMC: Adaptive kriging for Bayesian inverse modeling.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends X -TMCMC: Adaptive kriging for Bayesian inverse modeling

Reference 166

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This paper cites An efficient and robust sampler for Bayesian inference: Transitional ensemble Markov chain Monte Carlo.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends An efficient and robust sampler for Bayesian inference: Transitional ensemble Markov chain Monte Carlo

Reference 167

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This paper cites A two -stage Bayesian model updating framework based on an iterative model reduction technique using modal responses.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends A two -stage Bayesian model updating framework based on an iterative model reduction technique using modal responses

Reference 168

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Reference 169

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This paper cites Bayesian model updating using hybrid Monte Carlo simulation with application to structural dynamic models with many uncertain parameters.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Bayesian model updating using hybrid Monte Carlo simulation with application to structural dynamic models with many uncertain parameters

Reference 170

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This paper cites Bayesian model updating of a full -scale finite element model with sensitivity-based clustering.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Bayesian model updating of a full -scale finite element model with sensitivity-based clustering

Reference 171

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This paper cites Finite element model updating using Hamiltonian Monte Carlo techniques.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Finite element model updating using Hamiltonian Monte Carlo techniques

Reference 172

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This paper cites Modified Hamiltonian Monte Carlo‐based Bayesian finite element model updating of steel truss bridge.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Modified Hamiltonian Monte Carlo‐based Bayesian finite element model updating of steel truss bridge

Reference 173

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This paper cites Hamiltonian Monte Carlo methods for subset simulation in reliability analysis.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Hamiltonian Monte Carlo methods for subset simulation in reliability analysis

Reference 174

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This paper cites Riemannian manifold Hamiltonian Monte Carlo based subset simulation for reliability analysis in non-Gaussian space.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Riemannian manifold Hamiltonian Monte Carlo based subset simulation for reliability analysis in non-Gaussian space

Reference 175

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This paper cites Markov chain Monte Carlo simulation using the DREAM software package: Theory, concepts, and MATLAB implementation.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Markov chain Monte Carlo simulation using the DREAM software package: Theory, concepts, and MATLAB implementation

Reference 176

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This paper cites Computational inference of vibratory system with incomplete modal information using parallel, interactive and adaptive Markov chains.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Computational inference of vibratory system with incomplete modal information using parallel, interactive and adaptive Markov chains

Reference 177

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This paper cites Bayesian model updating for structural dynamic applications combing differential evolution adaptive Metropolis and kriging model.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Bayesian model updating for structural dynamic applications combing differential evolution adaptive Metropolis and kriging model

Reference 178

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This paper cites Sampling -based adaptive Bayesian quadrature for probabilistic model updating.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Sampling -based adaptive Bayesian quadrature for probabilistic model updating

Reference 179

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Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Probabilistic model updating via variational Bayesian inference and adaptive Gaussian process modeling

Reference 180

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This paper cites Data-driven approach for post-earthquake condition and reliability assessment with approximate Bayesian computation.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Data-driven approach for post-earthquake condition and reliability assessment with approximate Bayesian computation

Reference 181

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This paper cites Probabilistic model updating of civil structures with a decentralized variational inference approach.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Probabilistic model updating of civil structures with a decentralized variational inference approach

Reference 182

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Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Efficient variational Bayesian model updating by Bayesian active learning

Reference 183

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Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Unresolved cited work

Reference 184

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Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Unresolved cited work

Reference 185

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This paper cites Cyclical variational Bayes Monte Carlo for efficient multi -modal posterior distributions evaluation.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Cyclical variational Bayes Monte Carlo for efficient multi -modal posterior distributions evaluation

Reference 186

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This paper cites StocIPNet: A novel probabilistic interpretable network with affine-embedded reparameterization layer for high -dimensional stochastic inverse problems.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends StocIPNet: A novel probabilistic interpretable network with affine-embedded reparameterization layer for high -dimensional stochastic inverse problems

Reference 187

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This paper cites Past, present and future of nonlinear system identification in structural dynamics.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Past, present and future of nonlinear system identification in structural dynamics

Reference 188

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Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Bayesian updating and identifiability assessment of nonlinear finite element models

Reference 189

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This paper cites Nonlinear finite element model updating for damage identification of civil structures using batch Bayesian estimation.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Nonlinear finite element model updating for damage identification of civil structures using batch Bayesian estimation

Reference 191

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This paper cites Bayesian optimal estimation for output-only nonlinear system and damage identification of civil structures.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Bayesian optimal estimation for output-only nonlinear system and damage identification of civil structures

Reference 192

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This paper cites Bayesian nonlinear structural FE model and seismic input identification for damage assessment of civil structures.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Bayesian nonlinear structural FE model and seismic input identification for damage assessment of civil structures

Reference 193

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This paper cites Bayesian updating and model class selection for hysteretic structural models using stochastic simulation.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Bayesian updating and model class selection for hysteretic structural models using stochastic simulation

Reference 194

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This paper cites Parameter estimation and model selection for a class of hysteretic systems using Bayesian inference.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Parameter estimation and model selection for a class of hysteretic systems using Bayesian inference

Reference 195

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This paper cites Bayesian system identification of a nonlinear dynamical system using a novel variant of simulated annealing.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Bayesian system identification of a nonlinear dynamical system using a novel variant of simulated annealing

Reference 196

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This paper cites Bayesian calibration of hysteretic parameters with consideration of the model discrepancy for use in seismic structural health monitoring.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Bayesian calibration of hysteretic parameters with consideration of the model discrepancy for use in seismic structural health monitoring

Reference 197

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Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Papadioti, C

Reference 198

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This paper cites Bayesian model updating of nonlinear systems using nonlinear normal modes.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Bayesian model updating of nonlinear systems using nonlinear normal modes

Reference 199

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This paper cites Bayesian model updating and class selection of a wing -engine structure with nonlinear connections using nonlinear normal modes.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Bayesian model updating and class selection of a wing -engine structure with nonlinear connections using nonlinear normal modes

Reference 200

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This paper cites Bayesian model identification of higher- order frequency response functions for structures assembled by bolted joints.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Bayesian model identification of higher- order frequency response functions for structures assembled by bolted joints

Reference 201

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This paper cites An online coupled state/input/parameter estimation approach for structural dynamics.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends An online coupled state/input/parameter estimation approach for structural dynamics

Reference 202

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This paper cites Parameter identification of a differentiable Bouc-Wen model using constrained extended Kalman filter.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Parameter identification of a differentiable Bouc-Wen model using constrained extended Kalman filter

Reference 203

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This paper cites The unscented Kalman filter and particle filter methods for nonlinear structural system identification with non-collocated heterogeneous sensing.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends The unscented Kalman filter and particle filter methods for nonlinear structural system identification with non-collocated heterogeneous sensing

Reference 204

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Observation 68d1b731-44dd-457d-8d2d-06c1ec1366df · outbound

This paper cites Material parameter identification in distributed plasticity FE models of frame-type structures using nonlinear stochastic filtering.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Material parameter identification in distributed plasticity FE models of frame-type structures using nonlinear stochastic filtering

Reference 205

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This paper cites An offline approach for output -only Bayesian identification of stochastic nonlinear systems using unscented Kalman filtering.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends An offline approach for output -only Bayesian identification of stochastic nonlinear systems using unscented Kalman filtering

Reference 206

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This paper cites Constrained unscented Kalman filter for parameter identification of structural systems.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Constrained unscented Kalman filter for parameter identification of structural systems

Reference 207

Resolution: verified exact; doi, observed 2026-08-07T13:18:33.489157Z

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This paper cites Dynamic strain estimation for fatigue assessment of an offshore monopile wind turbine using filtering and modal expansion 128 algorithms.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Dynamic strain estimation for fatigue assessment of an offshore monopile wind turbine using filtering and modal expansion 128 algorithms

Reference 208

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This paper cites Joint input-response estimation for structural systems based on reduced-order models and vibration data from a limited number of sensors.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Joint input-response estimation for structural systems based on reduced-order models and vibration data from a limited number of sensors

Reference 209

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This paper cites An augmented Kalman filter for force identification in structural dynamics.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends An augmented Kalman filter for force identification in structural dynamics

Reference 210

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This paper cites A dual Kalman filter approach for state estimation via output-only acceleration measurements.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends A dual Kalman filter approach for state estimation via output-only acceleration measurements

Reference 211

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This paper cites Online correction of drift in structural identification using artificial white noise observations and an unscented Kalman filter.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Online correction of drift in structural identification using artificial white noise observations and an unscented Kalman filter

Reference 212

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This paper cites Stable force identification in structural dynamics using Kalman filtering and dummy -measurements.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Stable force identification in structural dynamics using Kalman filtering and dummy -measurements

Reference 213

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This paper cites Real‐time system identification: an algorithm for simultaneous model class selection and parametric identification.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Real‐time system identification: an algorithm for simultaneous model class selection and parametric identification

Reference 214

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This paper cites Identifiability‐enhanced Bayesian frequency‐domain substructure identification.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Identifiability‐enhanced Bayesian frequency‐domain substructure identification

Reference 215

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This paper cites A dual adaptive filtering approach for nonlinear finite element model updating accounting for modeling uncertainty.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends A dual adaptive filtering approach for nonlinear finite element model updating accounting for modeling uncertainty

Reference 216

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This paper cites Joint parameter -input estimation for digital twinning of the Block Island wind turbine using output -only measurements.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Joint parameter -input estimation for digital twinning of the Block Island wind turbine using output -only measurements

Reference 217

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This paper cites Adaptive Kalman filters for nonlinear finite element model updating.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Adaptive Kalman filters for nonlinear finite element model updating

Reference 218

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This paper cites Adaptive Bayesian inference framework for joint model and noise identification.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Adaptive Bayesian inference framework for joint model and noise identification

Reference 219

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This paper cites A variational Bayesian inference technique for model updating of structural systems with unknown noise statistics.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends A variational Bayesian inference technique for model updating of structural systems with unknown noise statistics

Reference 220

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This paper cites Bayesian finite element model inversion of offshore wind turbine structures for joint parameter -load estimation.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Bayesian finite element model inversion of offshore wind turbine structures for joint parameter -load estimation

Reference 221

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This paper cites Extended Kalman filter for material parameter estimation in nonlinear structural finite element models using direct differentiation method.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Extended Kalman filter for material parameter estimation in nonlinear structural finite element models using direct differentiation method

Reference 222

Resolution: verified exact; doi, observed 2026-08-07T13:18:32.219265Z

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This paper cites Dual estimation of partially observed nonlinear structural systems: a particle filter approach.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Dual estimation of partially observed nonlinear structural systems: a particle filter approach

Reference 223

Resolution: verified exact; doi, observed 2026-08-07T13:18:32.059391Z

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This paper cites Tracking of inputs, states and parameters of linear structural dynamic systems.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Tracking of inputs, states and parameters of linear structural dynamic systems

Reference 224

Resolution: verified exact; doi, observed 2026-08-07T13:18:31.932506Z

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This paper cites Data fusion based EKF -UI for real -time simultaneous identification of structural systems and unknown external inputs.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Data fusion based EKF -UI for real -time simultaneous identification of structural systems and unknown external inputs

Reference 225

Resolution: verified exact; doi, observed 2026-08-07T13:18:31.822569Z

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This paper cites A novel unscented Kalman filter for recursive state- input-system identification of nonlinear systems.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends A novel unscented Kalman filter for recursive state- input-system identification of nonlinear systems

Reference 226

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This paper cites Input-state-parameter estimation of structural systems from limited output information.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Input-state-parameter estimation of structural systems from limited output information

Reference 227

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This paper cites Auto -regressive model based input and parameter estimation for nonlinear finite element models.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Auto -regressive model based input and parameter estimation for nonlinear finite element models

Reference 228

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This paper cites On the application of Gaussian process latent force models for joint input-state-parameter estimation: With a view to Bayesian operational identification.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends On the application of Gaussian process latent force models for joint input-state-parameter estimation: With a view to Bayesian operational identification

Reference 229

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This paper cites Real-time simultaneous input -state-parameter estimation with modulated colored noise excitation.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Real-time simultaneous input -state-parameter estimation with modulated colored noise excitation

Reference 230

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This paper cites Online updating and uncertainty quantification using nonstationary output-only measurement.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Online updating and uncertainty quantification using nonstationary output-only measurement

Reference 231

Resolution: verified exact; doi, observed 2026-08-07T13:18:31.383171Z

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This paper cites Identification and uncertainty estimation of structural parameters.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Identification and uncertainty estimation of structural parameters

Reference 232

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This paper cites Covariance matching based adaptive unscented Kalman filter for direct filtering in INS/GNSS integration.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Covariance matching based adaptive unscented Kalman filter for direct filtering in INS/GNSS integration

Reference 233

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This paper cites A generalized autocovariance least-squares method for Kalman filter tuning.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends A generalized autocovariance least-squares method for Kalman filter tuning

Reference 234

Resolution: verified exact; doi, observed 2026-08-07T13:18:31.235159Z

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This paper cites Joint input -state estimation in structural dynamics.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Joint input -state estimation in structural dynamics

Reference 235

Resolution: verified exact; doi, observed 2026-08-07T13:18:31.060630Z

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This paper cites Selection of noise parameters for Kalman filter.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Selection of noise parameters for Kalman filter

Reference 236

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This paper cites Online estimation of noise parameters for Kalman filter.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Online estimation of noise parameters for Kalman filter

Reference 237

Resolution: verified exact; doi, observed 2026-08-07T13:18:30.664240Z

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This paper cites Estimation of time-varying noise parameters for unscented Kalman filter.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Estimation of time-varying noise parameters for unscented Kalman filter

Reference 238

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Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Input -state-parameter-noise identification and virtual sensing in dynamical systems: A Bayesian expectation -maximization (BEM) perspective

Reference 239

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Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Unresolved cited work

Reference 240

Resolution: verified exact; doi, observed 2026-08-07T13:18:33.632840Z

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This paper cites Hierarchical Bayesian modeling framework for model updating and robust predictions in structural dynamics using modal features.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Hierarchical Bayesian modeling framework for model updating and robust predictions in structural dynamics using modal features

Reference 241

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This paper cites Hierarchical Bayesian model updating for structural identification.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Hierarchical Bayesian model updating for structural identification

Reference 242

Resolution: verified exact; doi, observed 2026-08-07T13:18:30.497442Z

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This paper cites Accounting for environmental variability, modeling errors, and parameter estimation uncertainties in structural identification.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Accounting for environmental variability, modeling errors, and parameter estimation uncertainties in structural identification

Reference 243

Resolution: unresolved; no resolver link, observed 2026-08-07T13:16:37.177997Z

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This paper cites Modeling error estimation and response prediction of a 10 -story building model through a hierarchical Bayesian model updating framework.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Modeling error estimation and response prediction of a 10 -story building model through a hierarchical Bayesian model updating framework

Reference 244

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This paper cites Bayesian multilevel model calibration for inverse problems under uncertainty with perfect data.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Bayesian multilevel model calibration for inverse problems under uncertainty with perfect data

Reference 246

Resolution: verified exact; doi, observed 2026-08-07T13:18:30.131086Z

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This paper cites A unified framework for multilevel uncertainty quantification in Bayesian inverse problems.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends A unified framework for multilevel uncertainty quantification in Bayesian inverse problems

Reference 247

Resolution: verified exact; doi, observed 2026-08-07T13:18:29.937908Z

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Observation cc8e8a9f-a2e3-4a06-b2ec-01bec123f177 · outbound

This paper cites Bayesian calibration of hysteretic reduced order structural models for earthquake engineering applications.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Bayesian calibration of hysteretic reduced order structural models for earthquake engineering applications

Reference 248

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This paper cites Hierarchical Bayesian modeling for calibration and uncertainty quantification of constitutive material models: Application to a uniaxial steel material model.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Hierarchical Bayesian modeling for calibration and uncertainty quantification of constitutive material models: Application to a uniaxial steel material model

Reference 249

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This paper cites An efficient hierarchical Bayesian framework for multiscale material modeling.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends An efficient hierarchical Bayesian framework for multiscale material modeling

Reference 250

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This paper cites Probabilistic hierarchical Bayesian framework for time-domain model updating and robust predictions.

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends Probabilistic hierarchical Bayesian framework for time-domain model updating and robust predictions

Reference 251

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