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A Link between Shock-wave Theory and Symmetry-reduced Stochastic Gradient Descent for Artificial Neural Networks

arXiv cs.LG · 2026-06-18 Cached

This paper establishes a mathematically rigorous connection between shock-wave theory and symmetry-quotiented learning dynamics of stochastic gradient descent, showing that after symmetry reduction and coarse-graining, the dynamics satisfy viscous Hamilton-Jacobi and Burgers-type equations with shock formation times controlled by loss curvature.

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