In this talk,  we present Hybrid-Cooperative (HYCO) learning, a novel strategy for constructing mathematical models of physical systems that unites physics-based modeling with machine learning. Traditional approaches are often polarized: data-driven neural networks can capture patterns but struggle with physical consistency, while high-fidelity PDE-based models ensure accuracy but are computationally demanding. HYCO bridges these paradigms by embedding them in a cooperative, game-theoretic framework that exploits their complementary strengths. By merging empirical patterns extracted from data with the structural knowledge encoded in physical laws, HYCO delivers robust and interpretable models of complex phenomena. Numerical experiments show that HYCO outperforms both purely data-driven and purely physics-based methods, particularly in regimes with sparse, noisy, or localized data, highlighting its potential as a next-generation tool for scientific computing.