Temporal Lifting as Latent-Space Regularization for Continuous-Time Flow Models in AI Systems

• URL: http://arxiv.org/abs/2510.09805v1
• Date: Fri, 10 Oct 2025 19:06:32 GMT
• Title: Temporal Lifting as Latent-Space Regularization for Continuous-Time Flow Models in AI Systems
• Authors: Jeffrey Camlin,
• Abstract summary: We present a latent-space formulation of adaptive temporal reparametrization for continuous-time dynamical systems.<n>From the standpoint of machine-learning dynamics, temporal lifting acts as a continuous-time normalization or time-warping operator.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a latent-space formulation of adaptive temporal reparametrization for continuous-time dynamical systems. The method, called *temporal lifting*, introduces a smooth monotone mapping $t \mapsto \tau(t)$ that regularizes near-singular behavior of the underlying flow while preserving its conservation laws. In the lifted coordinate, trajectories such as those of the incompressible Navier-Stokes equations on the torus $\mathbb{T}^3$ become globally smooth. From the standpoint of machine-learning dynamics, temporal lifting acts as a continuous-time normalization or time-warping operator that can stabilize physics-informed neural networks and other latent-flow architectures used in AI systems. The framework links analytic regularity theory with representation-learning methods for stiff or turbulent processes.

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