Surgical Hill-Kinetics Field Saturation: Resolving Finite-Time Explosion and NFE Stiffness in Neural Ordinary Differential Equations

Sjfu

doi:10.5281/zenodo.21779034

August 3, 2026Open Access

Surgical Hill-Kinetics Field Saturation: Resolving Finite-Time Explosion and NFE Stiffness in Neural Ordinary Differential Equations

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Key Points

This research aims to address numerical stiffness and gradient blowup in neural networks using a new method.
Propose Surgical Hill-Kinetics Field Saturation to mitigate numerical issues in Neural ODEs.
Analyze performance under polynomial weight accumulation scenarios.
Successfully resolved finite-time explosions in simulated scenarios.
Reduced instances of gradient blowup, enhancing numerical stability.

Implication

Demonstrates resolution of numerical stiffness in neural networks, indicating potential improvements in computational stability.

Abstract

Continuous-Time Neural Networks and Neural Ordinary Differential Equations (Neural ODEs) suffer from severe numerical stiffness and finite-time gradient blowup under polynomial weight accumulation, leading to adaptive solver grid collapse (NFE -> infinity), loss oscillations, and machine-precision failures. We propose Surgical Hill-Kinetics Field Saturation...

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Cite This Study

Sjfu (2026) studied this question.

synapsesocial.com/papers/6a72e841226790f370657e58 https://doi.org/10.5281/zenodo.21779034

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Surgical Hill-Kinetics Field Saturation: Resolving Finite-Time Explosion and NFE Stiffness in Neural Ordinary Differential Equations

Key Points

Implication

Abstract

Discussion

Cite This Study

Surgical Hill-Kinetics Field Saturation: Resolving Finite-Time Explosion and NFE Stiffness in Neural Ordinary Differential Equations

Key Points

Implication

Abstract

Discussion

Cite This Study

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