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numerical solution a serious computational challenge. This project aims to tackle that head-on by developing adaptive numerical schemes powered by advanced nonlinear approximations—like Gaussian mixtures and
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neural networks under symmetry constraints, their optimization dynamics, and their generalization behavior—particularly in low-data or out-of-distribution settings. The work combines formal theoretical
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