The mass density of Earth's thermosphere (∼ 90–600 km altitude) is a critical parameter for
low Earth orbit prediction because of the atmospheric drag on satellites in this region. In this …

Computer simulation experiments are essential to modern scientific discovery, whether that
be in physics, chemistry, biology, epidemiology, ecology, engineering, etc. Surrogates are …

Satellite drag modeling remains the largest source of uncertainty affecting space operations
in low Earth orbit. The uncertainty stems from inaccurate models for mass density and drag …

Abstract Deep Gaussian processes (DGPs) are increasingly popular as predictive models in
machine learning for their nonstationary flexibility and ability to cope with abrupt regime …

Atmospheric mass density estimates derived from accelerometers onboard satellites such as
CHAllenging Minisatellite Payload (CHAMP) and Gravity Recovery and Climate Experiment …

Abstract Deep Gaussian processes (DGPs) upgrade ordinary GPs through functional
composition, in which intermediate GP layers warp the original inputs, providing flexibility to …

Atmospheric drag is the largest source of uncertainty in accurately predicting the orbit of
satellites in low Earth orbit (LEO). Accurately predicting drag for objects that traverse LEO is …

Many scientific phenomena are studied using computer experiments consisting of multiple
runs of a computer model while varying the input settings. Gaussian processes (GPs) are a …

We provide a survey of non-stationary surrogate models which utilize Gaussian processes
(GPs) or variations thereof, including non-stationary kernel adaptations, partition and local …

Bayesian Generative AI (BayesGen-AI) methods are developed and applied to Bayesian
computation. BayesGen-AI reconstructs the posterior distribution by directly modeling the …