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This paper introduces SO(3) Equivariant Neural Kalman Networks (SENK) for accurate and transferable vibrational spectral prediction, outperforming existing methods and integrating tensor prediction with physics-informed calibration.
This paper introduces the ⋆_G tensor algebra, a framework that makes equivariance an intrinsic algebraic property rather than an architectural constraint, providing provably-optimal symmetry-preserving tensor approximation, Kronecker factorization for composing multiple symmetries, and a Lean 4 formalization. Experiments on QM9 molecular geometry demonstrate data-driven discovery of physical symmetry selection rules.