9 papers
Koopman early warning signals for bifurcation and rate-induced tipping
Juan Nathaniel, Carla Roesch, Derek DeSantis +5
Abrupt transitions in complex systems are often preceded by early warning signals. However, most indicators rely on the notion of critical slowing down and do not generally extend…
Learning more physically realistic dynamics in machine-learning based weather forecasting with latent-space constraints
Hang Fan, Yi Xiao, Yongquan Qu +5
Data-driven machine learning (ML) models are reshaping weather forecasting and have shown the potential to accelerate and surpass traditional physics-based approaches, leading to a…
Wavelet Flow Matching for Multi-Scale Physics Emulation
Gabriele Accarino, Juan Nathaniel, Carla Roesch +4
Accurate emulation of multi-scale physical systems governed by PDEs demands models that remain stable over long autoregressive rollouts while preserving fine-scale structures. Dete…
In-context learning to predict critical transitions in dynamical systems
Yunus Sevinchan, Juan Nathaniel, Kai Ueltzhöffer +8
Critical transitions - abrupt, often irreversible changes in system dynamics - arise across human and natural systems, often with catastrophic consequences. Real-world observations…
Accurate and Efficient Hybrid-Ensemble Atmospheric Data Assimilation in Latent Space with Uncertainty Quantification
Hang Fan, Juan Nathaniel, Yi Xiao +5
Data assimilation (DA) combines model forecasts and observations to estimate the optimal state of the atmosphere with its uncertainty, providing initial conditions for weather pred…
CausalDynamics: A large-scale benchmark for structural discovery of dynamical causal models
Benjamin Herdeanu, Juan Nathaniel, Carla Roesch +4
Causal discovery for dynamical systems poses a major challenge in fields where active interventions are infeasible. Most methods used to investigate these systems and their associa…