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Requirement-Induced Predictive Geometry for Finite-Resource Prediction in Dynamical Systems
In nonlinear prediction, two state representations with the same local uncertainty volume can have radically different predictive value. We consider differentiable finite-time dynamics together with a quadratic terminal requirement that specifies which terminal state differences matter. Pulling this
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