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A blog post explaining Hamiltonian Neural Networks through differential geometry, using a simple mass-spring system to demonstrate how imposing conservation laws via network architecture can lead to more efficient learning. The author builds up mathematical tools like symplectic manifolds and Poisson brackets from basic calculus.