Lotka-Sharpe Neural Operators for Control of Population PDEs
Age-structured predator-prey integro-partial differential equations provide models of interacting populations in ecology, epidemiology, and biotechnology. A key challenge in feedback design for these systems is the scalar $ζ$, defined implicitly by the Lotka-Sharpe nonlinear integral condition, as a mapping from fertility and mortality rates to $ζ$. To solve this challenge with operator learning, we first prove that the Lotka-Sharpe operator is Lipschitz continuous, guaranteeing the existence of arbitrarily accurate neural operator approximations over a compact set of fertility and mortality functions. We then show that the resulting approximate feedback law preserves semi-global practica...
| Authors | Miroslav Krstic, Iasson Karafyllis, Luke Bhan et al. |
|---|---|
| Primary category | eess.SY |
| Published | 4 April 2026 |
| Last updated at source | 4 April 2026 |
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