Causal Discovery from Heteroscedastic Stochastic Dynamical Systems under Imperfect Physical Models
arXiv:2602.04907v2 Announce Type: replace Abstract: Causal discovery is a data-driven paradigm for analyzing complex systems, while physics-based models, such as ordinary differential equations (ODEs), provide mechanistic structure for real-world dynamical processes. Integrating these paradigms can improve identifiability, stability, and robustness. However, real dynamical systems often exhibit cyclic interactions and nonstationarity, whereas many causal discovery methods rely on acyclicity, stationarity, or equilibrium assumptions. We propose an integrative causal discovery framework for dyna
This research builds on contemporary advancements in AI and machine learning, particularly in causal inference, to address the long-standing challenge of complex dynamical systems.
This development allows for more robust and accurate modeling of complex systems by integrating data-driven causal discovery with physics-based models, improving predictability and control.
The ability to accurately model heteroscedastic stochastic dynamical systems under imperfect physical models will enhance the reliability of AI systems in real-world applications.
- · AI researchers
- · Complex systems engineers
- · Data scientists
- · Industries relying on predictive modeling
- · Systems relying on oversimplified causal models
- · Manual hypothesis testing
Improved understanding and control of complex real-world phenomena from climate to markets.
Reduced errors and increased efficiency in AI-driven decision-making processes across various sectors.
Acceleration of scientific discovery by automating and refining the identification of causal relationships in new domains.
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