85 citations · 107 across the 23 of their papers we have counts for
5 papers · 1 filter
Physics-Informed Priors with Application to Boundary Layer Velocity
Luca Menicali, David H. Richter, Stefano Castruccio
One of the most popular recent areas of machine learning predicates the use of neural networks augmented by information about the underlying process in the form of Partial Differen…
A Physics-Informed, Deep Double Reservoir Network for Forecasting Boundary Layer Velocity
Matthew Bonas, David H. Richter, Stefano Castruccio
When a fluid flows over a solid surface, it creates a thin boundary layer where the flow velocity is influenced by the surface through viscosity, and can transition from laminar to…
A Neural Network-Based Approach to Normality Testing for Dependent Data
Minwoo Kim, Marc G Genton, Raphael Huser +1
There is a wide availability of methods for testing normality under the assumption of independent and identically distributed data. When data are dependent in space and/or time, ho…
Calibrated Forecasts of Quasi-Periodic Climate Processes with Deep Echo State Networks and Penalized Quantile Regression
Matthew Bonas, Christopher K. Wikle, Stefano Castruccio
Among the most relevant processes in the Earth system for human habitability are quasi-periodic, ocean-driven multi-year events whose dynamics are currently incompletely characteri…
High Resolution Global Precipitation Downscaling with Latent Gaussian Models and Nonstationary SPDE Structure
Jiachen Zhang, Matthew Bonas, Diogo Bolster +2
Obtaining high-resolution maps of precipitation data can provide key insights to stakeholders to assess a sustainable access to water resources at urban scale. Mapping a nonstation…