Growth-Entropy Constraints and Regime Commitment in Nonlinear Dynamical Systems

Nonlinear dynamical systems often exhibit abrupt regime transitions in which predictability, stability, and system behavior change discretely rather than smoothly in time. In many applied and theoretical contexts—most notably atmospheric forecasting and cosmology—these transitions are poorly captured by time-indexed descriptions alone, leading to persistent interpretive and operational failures. This work introduces a constraint-based diagnostic framework in which system evolution is indexed by accumulated irreversible structural growthrather than by coordinate time. Growth is defined as the formation of persistent organization that restricts accessible microstates and is fundamentally constrained by entropy production. Sustained growth requires sufficient entropy export capacity, which acts as a boundary condition on irreversibility rather than a driving force or feedback mechanism. Within this growth–entropy framework, regime transitions correspond to the onset of sustained irreversible structure, while regime commitmentoccurs when alternative futures become dynamically inaccessible due to state-space contraction. This perspective explains abrupt ensemble collapse, suppression of spurious intensification signals, and increased robustness to perturbations without modifying governing equations or extending predictability beyond chaos limits. The framework is applied diagnostically in shadow mode to nonlinear atmospheric forecasting, where it provides early identification of regime lock-in while remaining fully compatible with existing numerical models. The same constraint logic is further shown to offer a consistent interpretive lens for cosmological phenomena, including inflation, horizon behavior, and late-time accelerated expansion, without altering general relativity or ΛCDM. This work is diagnostic rather than dynamical. It introduces no new forces or equations, but instead provides a unifying coordinate for understanding when nonlinear systems become structurally committed and why predictability changes discretely rather than continuously.