5 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…
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…
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…
Deep Koopman operator framework for causal discovery in nonlinear dynamical systems
Juan Nathaniel, Carla Roesch, Jatan Buch +4
We use a deep Koopman operator-theoretic formalism to develop a novel causal discovery algorithm, Kausal. Causal discovery aims to identify cause-effect mechanisms for better scien…