5 citations · 8 across the 6 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
Using Echo State Networks to Inform Physical Models for Fire Front Propagation
Myungsoo Yoo, Christopher K. Wikle
Wildfires can be devastating, causing significant damage to property, ecosystem disruption, and loss of life. Forecasting the evolution of wildfire boundaries is essential to real-…
A Bayesian Spatio-Temporal Level Set Dynamic Model and Application to Fire Front Propagation
Myungsoo Yoo, Christopher K. Wikle
Intense wildfires impact nature, humans, and society, causing catastrophic damage to property and the ecosystem, as well as the loss of life. Forecasting wildfire front propagation…