5 papers
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…
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…
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…
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…