3 citations · 4 across the 9 of their papers we have counts for
4 papers · 1 filter
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…
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…
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…
Generative emulation of chaotic dynamics with coherent prior
Juan Nathaniel, Pierre Gentine
Data-driven emulation of nonlinear dynamics is challenging due to long-range skill decay that often produces physically unrealistic outputs. Recent advances in generative modeling…