3 papers
math.DS2026
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…
cs.LG2026
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…
cs.LG2026
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…