AI RESEARCH
Causal Discovery from Heteroscedastic Stochastic Dynamical Systems under Imperfect Physical Models
arXiv CS.LG
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ArXi:2602.04907v2 Announce Type: replace 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.