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math.DS2026
From trends to optimized forecasts: Quantifying the skill and advancing the utility of dynamics-based early warnings for tipping events
Franco Du Plessis, Victoria Volodina, Chris A. Boulton +5
In this paper we explore and extend the state of the art for understanding the skill and utility of early warnings of critical transitions (tipping points) in forced nonlinear syst…
math.DS2025
On the dimension of pullback attractors in recurrent neural networks
Muhammed Fadera
Recurrent neural networks trained via the reservoir computing paradigm have demonstrated remarkable success in learning and reconstructing attractors from chaotic systems, often re…