Automatic Functional Differentiation in JAX
-
Updated
Sep 18, 2025 - Python
Automatic Functional Differentiation in JAX
First-variation boundary audit showing that the written GP-2 interval action fails literal boundary closure.
An intuitive derivation of smoothing splines from variational calculus, demonstrating their relationship to reproducing kernel Hilbert spaces (RKHS) and regularized neural networks.
Lagrangian mechanics and variational-calculus report artifacts.
Convention-fixed literal-interval repair that closes the warped action at first variation and on the exact background.
Add a description, image, and links to the variational-calculus topic page so that developers can more easily learn about it.
To associate your repository with the variational-calculus topic, visit your repo's landing page and select "manage topics."