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[2602.13847] Causally constrained reduced-order neural models of complex turbulent dynamical systems
Summary
This paper presents a novel framework for developing reduced-order neural models that accurately capture complex turbulent dynamical systems, particularly in climate dynamics, by enforcing causal constraints.
Why It Matters
Understanding and predicting complex turbulent systems, such as climate dynamics, is critical for addressing climate change and improving weather forecasting. This research enhances the reliability of neural network models by ensuring they adhere to causal relationships, which is essential for accurate simulations and predictions.
Key Takeaways
- Introduces a framework to suppress noncausal dependencies in neural models.
- Demonstrates application using the stochastic Charney-DeVore model for climate dynamics.
- Causal constraints improve model responses to external forcings.
- Framework is broadly applicable to various turbulent dynamical systems.
- Can be integrated into existing neural network architectures.
Nonlinear Sciences > Chaotic Dynamics arXiv:2602.13847 (nlin) [Submitted on 14 Feb 2026] Title:Causally constrained reduced-order neural models of complex turbulent dynamical systems Authors:Fabrizio Falasca, Laure Zanna View a PDF of the paper titled Causally constrained reduced-order neural models of complex turbulent dynamical systems, by Fabrizio Falasca and Laure Zanna View PDF HTML (experimental) Abstract:We introduce a flexible framework based on response theory and score matching to suppress spurious, noncausal dependencies in reduced-order neural emulators of turbulent systems, focusing on climate dynamics as a proof-of-concept. We showcase the approach using the stochastic Charney-DeVore model as a relevant prototype for low-frequency atmospheric variability. We show that the resulting causal constraints enhance neural emulators' ability to respond to both weak and strong external forcings, despite being trained exclusively on unforced data. The approach is broadly applicable to modeling complex turbulent dynamical systems in reduced spaces and can be readily integrated into general neural network architectures. Subjects: Chaotic Dynamics (nlin.CD); Statistical Mechanics (cond-mat.stat-mech); Machine Learning (cs.LG); Atmospheric and Oceanic Physics (physics.ao-ph) Cite as: arXiv:2602.13847 [nlin.CD] (or arXiv:2602.13847v1 [nlin.CD] for this version) https://doi.org/10.48550/arXiv.2602.13847 Focus to learn more arXiv-issued DOI via DataCite (pending registrat...