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Reduced-order models (ROMs) have become indispensable tools for reducing the computational complexity of high-fidelity simulations in science and engineering. In this thesis, we introduce a novel training framework that combines the integral form of Operator Inference (OpInf ) with adjoint-state methods to yield robust, data-driven ROMs. By formulating a continuous-time loss functional that integr
