Automatic Functional Differentiation in JAX
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Updated
Sep 18, 2025 - Python
Automatic Functional Differentiation in JAX
Lagrangian mechanics and variational-calculus report artifacts.
Contains implementation of the Deep-Ritz Method for solving variational problems defined on intervals.
An intuitive derivation of smoothing splines from variational calculus, demonstrating their relationship to reproducing kernel Hilbert spaces (RKHS) and regularized neural networks.
First-variation boundary audit showing that the written GP-2 interval action fails literal boundary closure.
Convention-fixed literal-interval repair that closes the warped action at first variation and on the exact background.
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