Exposes a dynamic-probabilistic consistency gap in chaotic dynamical systems reconstruction and introduces the KAFFEE differentiable extended Kalman filter training framework to address it.
Auto-differentiable data assimilation: Co-learning of states, dynamics, and filtering algorithms.arXiv preprint arXiv:2603.20891, 2026
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The Dynamic-Probabilistic Consistency Gap in Chaotic Surrogate Modeling
Exposes a dynamic-probabilistic consistency gap in chaotic dynamical systems reconstruction and introduces the KAFFEE differentiable extended Kalman filter training framework to address it.