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

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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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-08-03
DOI
https://doi.org/10.5281/zenodo.21779035
Primary Topic
Model Reduction and Neural Networks
Type
preprint
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preprint

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

Sjfu
Zenodo (CERN European Organization for Nuclear Research)
Model Reduction and Neural Networks
preprint

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

Sjfu
preprint en

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...

Zenodo (CERN European Organization for Nuclear Research)
Nexen (Canada) (CA)
Sustainable cities and communities
Model Reduction and Neural Networks
DOIOpen Access PDFOpenAlex
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Surgical Hill-Kinetics Field Saturation: Resolving Finite-Time Explosion and NFE Stiffness in Neural Ordinary Differential Equations — Sjfu · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS