arXiv subjects

Joshua D. Pribe

Publications and source records attributed to Joshua D. Pribe.

2 recordsLinked to original sources

arXiv2026

Probabilistic calibration of crystal plasticity material models with synthetic global and local data

Crystal plasticity models connect macroscopic deformation with the physics of microscale slip in polycrystalline materials. These models can be calibrated using global stress-strain curves, but the resulting parametrization is often not unique: multiple parametrizations can predict the same global behavior but different local, grain-scale behavior. Using local data for calibration can mitigate uniqueness issues, but expensive specialized experiments like high-energy X-ray diffraction (HEDM) are typically required to gather the data. The computational expense of full-field simulations also often prevents uncertainty quantification with sampling-based calibration algorithms like Markov chain Monte Carlo. This study presents a two-stage calibration procedure that combines global and local data and balances the efficiency of a surrogate model with the accuracy of full-field crystal plasticity simulations. The procedure quantifies uncertainty using Bayesian inference with an efficient, parallelized sequential Monte Carlo algorithm. Calibrations are completed using synthetic data with a microstructure representative of Inconel 718 to assess uncertainty and accuracy of the parameters relative to a known ground truth. Global data comes from the uniaxial stress-strain curve, while local data comes from grain-average stresses, reflecting typical outputs of HEDM experiments. Additional calibrations with limited and noisy local data demonstrate robustness of the procedure and identify the most important features of the data. Overall, the results demonstrate the computational efficiency of the two-stage procedure and the value of local data for reducing parameter uncertainty. In addition, joint distributions of the calibrated parameters highlight key considerations in choosing constitutive models and calibration data, including challenges resulting from correlated parameters.

cond-mat.mtrl-sci↗

arXiv2025

Latent Generative Modeling of Random Fields from Limited Training Data

The ability to accurately model random fields plays a critical role in science and engineering for problems involving uncertain, spatially-varying quantities such as heterogeneous material properties and turbulent flows. Deep generative models offer a powerful tool for sampling high- or infinite-dimensional uncertainties like random fields, but their reliance on large, dense training datasets limits their applicability in contexts where sufficient data is difficult or expensive to obtain. In this work, we propose a latent-space approach to generative modeling of random fields that incorporates domain knowledge to supplement limited training data. A constraint-aware variational autoencoder (VAE) with a function decoder is first used to learn compact latent representations of continuous functions that adhere to known physical or statistical constraints, even when training data is sparse or indirect. Generative modeling is then performed in the learned latent space, decoupling constraint enforcement from the sampling process. This decoupling enables expressive multi-step generative methods to be deployed in data-limited settings where existing constrained multi-step approaches are not directly applicable. The richer latent distributions captured by the generative model also overcome limitations of standard VAEs, which rely on simple parametric priors and struggle to represent complex, multimodal, or heavy-tailed distributions over functions. Efficacy is demonstrated on two challenging applications: wind velocity field reconstruction from sparse sensors and material property inference from indirect measurements. Results show the effectiveness of incorporating domain knowledge constraints for data-limited problems and the improved sample quality and robustness of the latent generative modeling approach versus directly sampling a constrained VAE.

cs.LG↗