6 citations · 6 across the 3 of their papers we have counts for
5 papers
A Response Theory Probe for Learned Stochastic AI Simulators, Tested on Lorenz-63
João Böger, Simon Driscoll, Niccolò Zagli +2
Machine-learning emulators of chaotic and stochastic systems are usually validated on forecast skill and long-run statistics. Neither certifies that an emulator responds correctly…
Evaluating Dynamical Fidelity through Predictive Structure in Physical Representations
Oskar Bohn Lassen, Joao Paulo de Souza Boger, Simon Driscoll +4
Machine-learning models for physical systems are currently evaluated primarily through errors between predicted and reference states and, increasingly, through tests of physical co…
Tackling "AI against sustainability"
Stefanie Kunkel, Malcolm Campbell-Verduyn, Simon Driscoll +2
Current debates on AI and sustainability are dichotomised, tending to focus on either the environmental impact of AI systems themselves ("sustainability of AI") or AI's potential f…
Investigating Inductive Biases for Machine Learning Emulation of Sudden Stratospheric Warmings in Idealised Isca Simulations
Oskar Bohn Lassen, Simon Driscoll, Stephen I. Thomson +2
Machine-learning emulators are increasingly used for weather prediction and have the potential to extend skill on subseasonal-to-seasonal timescales by learning dynamically importa…
Do AI models produce better weather forecasts than physics-based models? A quantitative evaluation case study of Storm Ciarán
Andrew J. Charlton-Perez, Helen F. Dacre, Simon Driscoll +8
There has been huge recent interest in the potential of making operational weather forecasts using machine learning techniques. As they become a part of the weather forecasting too…