Author

Song-Ju Kim

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Preprint Sep 2026

Initial-State Precision as a Predictive Resource: From Tori to Strange Nonchaotic Attractors and Chaos

How much initial-state precision is required to predict a nonlinear system to a prescribed accuracy over a finite horizon? We formulate this inverse prediction problem through a one-shot resource $B_N$, defined as the number of binary refinement bits required in the initial state by a specified local sensing architectu...

Song-Ju Kim · 0 citations

Preprint Sep 2026

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 requirement back th...

Song-Ju Kim · 2 citations