4 papers
Making Recursive Bayesian Inference Robust
Myungsoo Yoo, Daniel Würzler Barreto, Mevin B. Hooten
While Bayesian inference has become increasingly popular with advances in computational resources, its algorithms can be computationally prohibitive and may not scale with large da…
A Statistician's Overview of Physics-Informed Neural Networks for Spatio-Temporal Data
Christopher K. Wikle, Joshua North, Giri Gopalan +1
The recent success of deep neural network models with physical constraints (so-called, Physics-Informed Neural Networks, PINNs) has led to renewed interest in the incorporation of…
Emulation with uncertainty quantification of regional sea-level change caused by the Antarctic Ice Sheet
Myungsoo Yoo, Giri Gopalan, Matthew J. Hoffman +4
Projecting sea-level change in various climate-change scenarios typically involves running forward simulations of the Earth's gravitational, rotational and deformational (GRD) resp…
Modeling high and low extremes with a novel dynamic spatio-temporal model
Myungsoo Yoo, Likun Zhang, Christopher K. Wikle +1
Extreme environmental events such as severe storms, drought, heat waves, flash floods, and abrupt species collapse have become more prevalent in the earth-atmosphere dynamic system…