11 papers
Accounting for variable detection functions in temporal abundance modeling via transfer learning
Kevin M. Collins, Erin M. Schliep, Tyler Wagner +1
Relative abundance, measured as the number of animals caught per unit of sampling effort (CPUE), is commonly used to monitor fish and wildlife populations, largely because sampling…
Echo State Networks for Spatio-Temporal Area-Level Data
Zhenhua Wang, Scott H. Holan, Christopher K. Wikle
Spatio-temporal area-level datasets play a critical role in official statistics, providing valuable insights for policy-making and regional planning. Accurate modeling and forecast…
Modeling Spatio-temporal Extremes via Conditional Variational Autoencoders
Xiaoyu Ma, Likun Zhang, Christopher K. Wikle
Extreme weather events are widely studied in fields such as agriculture, ecology, and meteorology. The spatio-temporal co-occurrence of extreme events can strengthen or weaken unde…
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…