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Data-Driven Stochastic Closure Modeling via Conditional Diffusion Model and Neural Operator
We propose a novel framework that leverages state-of-the-art machine learning techniques to construct stochastic closure models. Our approach combines conditional diffusion models with the Fourier Neural Operator (FNO) to capture the missing dynamics in complex systems governed by partial differential equations (PDEs).