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Discretizing Continuous ML Models: Offline Ballistic Coefficient Corrections via Lookup Table Approximation

An academic examination of discretizing continuous machine learning models into offline lookup tables for ballistic coefficient corrections. This paper presents a methodology for converting ML-derived velocity-dependent BC corrections into caliber-specific binary tables, enabling accurate trajectory predictions without network connectivity. We analyze the trade-offs between approximation fidelity and practical utility, demonstrating that piecewise-linear interpolation over a 5-dimensional parameter space achieves sub-5% deviation from continuous ML predictions across most of the flight envelope, with predictable degradation in transonic regions where non-linearities dominate.