1 citations · 1 across the 5 of their papers we have counts for
5 papers
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…
A flexible class of priors for orthonormal matrices with basis function-specific structure
Joshua S. North, Mark D. Risser, F. Jay Breidt
Statistical modeling of high-dimensional matrix-valued data motivates the use of a low-rank representation that simultaneously summarizes key characteristics of the data and enable…
A Review of Data-Driven Discovery for Dynamic Systems
Joshua S. North, Christopher K. Wikle, Erin M. Schliep
Many real-world scientific processes are governed by complex nonlinear dynamic systems that can be represented by differential equations. Recently, there has been increased interes…
A Bayesian Approach for Spatio-Temporal Data-Driven Dynamic Equation Discovery
Joshua S. North, Christopher K. Wikle, Erin M. Schliep
Differential equations based on physical principals are used to represent complex dynamic systems in all fields of science and engineering. Through repeated use in both academics a…
On the spatial and temporal shift in the archetypal seasonal temperature cycle as driven by annual and semi-annual harmonics
Joshua S. North, Erin M. Schliep, Christopher K. Wikle
Statistical methods are required to evaluate and quantify the uncertainty in environmental processes, such as land and sea surface temperature, in a changing climate. Typically, an…