7 papers
Modeling Falling Backgrounds with Exponential Mixtures
Austin Townsend, Marc Osherson, Mike Hildreth +1
Searches for new physics at the LHC often look for localized excesses on smoothly falling background distributions. Several classes of background models have been considered, inclu…
Mixture-of-Finite-Mixtures Wishart Model for Clustering Covariance Matrices with an Application to Brain Functional Connectivity
Zongyu Li, Stefano Castruccio, Zhiyong Zhang
Data represented as covariance-type matrices arise in many fields, including brain functional connectivity and diffusion tensor imaging. We develop the MFM-Wishart, a Bayesian mode…
A Physics-Informed Spatiotemporal Deep Learning Framework for Turbulent Systems
Luca Menicali, Andrew Grace, David H. Richter +1
Fluid thermodynamics underpins atmospheric dynamics, climate science, industrial applications, and energy systems. However, direct numerical simulations (DNS) of such systems can b…
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…
CESAR: A Convolutional Echo State AutoencodeR for High-Resolution Wind Forecasting
Matthew Bonas, Paolo Giani, Paola Crippa +1
An accurate and timely assessment of wind speed and energy output allows an efficient planning and management of this resource on the power grid. Wind energy, especially at high re…
Modeling High-Resolution Spatio-Temporal Wind with Deep Echo State Networks and Stochastic Partial Differential Equations
Kesen Wang, Minwoo Kim, Stefano Castruccio +1
In the past decades, clean and renewable energy has gained increasing attention due to a global effort on carbon footprint reduction. In particular, Saudi Arabia is gradually shift…