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Dynamical Systems Reconstruction
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dynamical-systems-reconstruction-b81c3419·1 events·first seen 16d agoAliases: Dynamical Systems Reconstruction
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4D reconstructionlatent dynamical systemsonline 3D reconstructiondifferentiable dynamics sensor calibrationPreserving Plasticity in Continual Learning via Dynamical IsometryThe Stable Recovery Manifold: Geometric Principles Governing Recoverability in Continual Learningmulti-view 3D reconstructiondynamics randomizationRecovery Subspace DimensionalityBehavioral Trajectory Tracking FrameworkState-Conditioned Dynamic SteeringLangevin Dynamics
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KAFFEE: Addressing the Dynamic-Probabilistic Consistency Gap in Chaotic Surrogate Modeling
This paper identifies a 'dynamic-probabilistic consistency (DPC) gap' in dynamical systems reconstruction (DSR), where optimizing finite-horizon probabilistic objectives can degrade learned dynamics or decouple predictive uncertainty from local tangent dynamics. Three failure mechanisms are isolated: core collapse, noise masking, and blind uncertainty. The authors propose KAFFEE, a differentiable extended Kalman filter-based training framework that evaluates likelihood on local predictive residuals while transporting covariance through learned Jacobians, reducing these failure modes on stochastic hyperchaotic Lorenz-96 and across 13 chaotic systems when adapting a DSR foundation model.