6 citations · 8 across the 5 of their papers we have counts for
6 papers
Probabilistic calibration of crystal plasticity material models with synthetic global and local data
Joshua D. Pribe, Patrick E. Leser, Saikumar R. Yeratapally +1
Calibrating full-field crystal plasticity (CP) models using global stress-strain curves alone often produces non-unique parametrizations: multiple parameter sets can predict the sa…
Bayesian Symbolic Regression via Posterior Sampling
Geoffrey F. Bomarito, Patrick E. Leser
Symbolic regression is a powerful tool for discovering governing equations directly from data, but its sensitivity to noise hinders its broader application. This paper introduces a…
Latent Generative Modeling of Random Fields from Limited Training Data
James E. Warner, Tristan A. Shah, Patrick E. Leser +3
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…
Entropy-based adaptive design for contour finding and estimating reliability
D. Austin Cole, Robert B. Gramacy, James E. Warner +3
In reliability analysis, methods used to estimate failure probability are often limited by the costs associated with model evaluations. Many of these methods, such as multifidelity…
On the Optimization of Approximate Control Variates with Parametrically Defined Estimators
Geoffrey F. Bomarito, Patrick E. Leser, James E. Warner +1
Multi-model Monte Carlo methods, such as multi-level Monte Carlo (MLMC) and multifidelity Monte Carlo (MFMC), allow for efficient estimation of the expectation of a quantity of int…
Inverse Estimation of Elastic Modulus Using Physics-Informed Generative Adversarial Networks
James E. Warner, Julian Cuevas, Geoffrey F. Bomarito +2
While standard generative adversarial networks (GANs) rely solely on training data to learn unknown probability distributions, physics-informed GANs (PI-GANs) encode physical laws…