Tim Whittaker
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View article: Constructing Extreme Heatwave Storylines with Differentiable Climate Models
Constructing Extreme Heatwave Storylines with Differentiable Climate Models Open
Understanding the plausible upper bounds of extreme weather events is essential for risk assessment in a warming climate. Existing methods, based on large ensembles of physics-based models, are often computationally expensive or lack the f…
View article: Evaluating Near‐Surface Wind Speeds Simulated by the CRCM6‐GEM5 Model Using AmeriFlux Data Over North America
Evaluating Near‐Surface Wind Speeds Simulated by the CRCM6‐GEM5 Model Using AmeriFlux Data Over North America Open
We evaluate the performance of various configurations of the Canadian Regional Climate Model (CRCM6‐GEM5) in simulating 10‐m wind speeds using data from 27 AmeriFlux stations across North America. The assessment employs a hierarchy of erro…
View article: Turbulence scaling from deep learning diffusion generative models
Turbulence scaling from deep learning diffusion generative models Open
Complex spatial and temporal structures are inherent characteristics of turbulent fluid flows and comprehending them poses a major challenge.This comprehesion necessitates an understanding of the space of turbulent fluid flow configuration…
View article: Turbulence Scaling from Deep Learning Diffusion Generative Models
Turbulence Scaling from Deep Learning Diffusion Generative Models Open
Complex spatial and temporal structures are inherent characteristics of turbulent fluid flows and comprehending them poses a major challenge. This comprehesion necessitates an understanding of the space of turbulent fluid flow configuratio…
View article: Neural Network Complexity of Chaos and Turbulence
Neural Network Complexity of Chaos and Turbulence Open
Chaos and turbulence are complex physical phenomena, yet a precise definition of the complexity measure that quantifies them is still lacking. In this work we consider the relative complexity of chaos and turbulence from the perspective of…
View article: Using machine learning to parametrize postmerger signals from binary neutron stars
Using machine learning to parametrize postmerger signals from binary neutron stars Open
There is growing interest in the detection and characterization of gravitational waves from postmerger oscillations of binary neutron stars. These signals contain information about the nature of the remnant and the high-density and out-of-…
View article: Probing High Energy Physics Through Gravitational Waves
Probing High Energy Physics Through Gravitational Waves Open
Over the last few years, gravitational wave detections have become ubiquitous, giving the
\nphysics community vast information about fundamental physics. As some of the universe’s
\nhighest energy events, neutron mergers allow us to explor…