.. title:: Talk: Algorithmic Differential Inversion

.. only:: html

Algorithmic Differential Inversion for Regularized Training of Neural Differential Equations
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Speaker: Gero Kauerauf, RWTH Aachen

Abstract
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Training Neural Differential Equations often causes severe trajectory ill-
conditioning and solver slowdowns. We present a matrix-free algorithmic
differential inversion approach that efficiently computes and regularizes
trajectory condition numbers during training. By expressing global linear solves
as sequential step-level inverse sweeps, our method bypasses dense matrix
storage or global iterative linear solves entirely. Experiments demonstrate
order-of-magnitude cost reductions and accelerated trajectory integration.