Machine Learning supported Bayesian inference on finite element input parameters to simulate uncertainty in progressive fracture tests of thin wood veneer laminates

This study presents an integrated framework for calibrating material parameters and their associated uncertainties in continuum finite element (FE) simulations of progressive damage in thin wood veneer laminates. Compact tension tests on two laminate layups, [ 0 / 90 ] 2 s and [ ± 45 ] 2 s , serve as the basis for calibrating FE input parameters both along and perpendicular to the grain direction. To enable efficient uncertainty quantification, a deep Long Short-Term Memory (LSTM) neural network is developed to rapidly predict full force vs displacement curves from these fracture tests. The FE input parameters are treated as random variables, and Bayesian inference with Markov Chain Monte Carlo sampling is applied to the LSTM surrogate models to estimate their distributions based on variability observed in experiments. Validation against experimental data demonstrates that the calibrated parameters accurately simulate damage progression, including uncertainty, in both compact tension and open-hole tension tests of quasi-isotropic [ 90 / 45 / 0 / − 45 ] s laminates, with mean prediction errors below 7%. • Development of integrated FEA–ML–Bayesian framework for progressive fracture simulation including uncertainties. • Curve-based uncertainty quantification using deep LSTM surrogate modelling for rapid model evaluation. • Experimentally validated prediction of variability across layups and mechanical tests.