6 citations · 9 across the 7 of their papers we have counts for
8 papers
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…
BISTRO -- A Bi-Fidelity Stochastic Gradient Framework using Trust-Regions for Optimization Under Uncertainty
Thomas O. Dixon, Geoffrey F. Bomarito, James E. Warner +1
Stochastic optimization of engineering systems is often infeasible due to repeated evaluations of a computationally expensive, high-fidelity simulation. Bi-fidelity methods mitigat…
Automated Model Tuning for Multifidelity Uncertainty Propagation in Trajectory Simulation
James E. Warner, Geoffrey F. Bomarito, Gianluca Geraci +1
Multifidelity uncertainty propagation combines the efficiency of low-fidelity models with the accuracy of a high-fidelity model to construct statistical estimators of quantities of…
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…
Covariance Expressions for Multi-Fidelity Sampling with Multi-Output, Multi-Statistic Estimators: Application to Approximate Control Variates
Thomas O. Dixon, James E. Warner, Geoffrey F. Bomarito +1
We provide a collection of results on covariance expressions between Monte Carlo based multi-output mean, variance, and Sobol main effect variance estimators from an ensemble of mo…
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…