HyCOP learns policies over compositions of hybrid modules to produce interpretable programs for parametric PDE solution operators with order-of-magnitude OOD gains over monolithic neural operators.
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Defines meta-Bayesian Nash equilibrium for incomplete information and proves existence via Kakutani's fixed point theorem assuming finite type spaces, meta-actions, transformations, and unique Bayesian Nash equilibria in transformed games.
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HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs
HyCOP learns policies over compositions of hybrid modules to produce interpretable programs for parametric PDE solution operators with order-of-magnitude OOD gains over monolithic neural operators.
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Meta-Bayesian Nash Equilibrium: Existence via Kakutani's Fixed Point Theorem
Defines meta-Bayesian Nash equilibrium for incomplete information and proves existence via Kakutani's fixed point theorem assuming finite type spaces, meta-actions, transformations, and unique Bayesian Nash equilibria in transformed games.