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This paper presents a deep learning approach that learns the Laplace transform of high-dimensional reflected Brownian motion (RBM) stationary distributions using the basic adjoint relationship. The method demonstrates near-perfect prediction in high-dimensional settings where analytical solutions are unavailable.