A Dynamical Systems Perspective on the Analysis of Neural Networks

• URL: http://arxiv.org/abs/2507.05164v1
• Date: Mon, 07 Jul 2025 16:18:49 GMT
• Title: A Dynamical Systems Perspective on the Analysis of Neural Networks
• Authors: Dennis Chemnitz, Maximilian Engel, Christian Kuehn, Sara-Viola Kuntz,
• Abstract summary: We utilize dynamical systems to analyze several aspects of machine learning algorithms.<n>We demonstrate how to re-formulate a variety of challenges from deep neural networks, (stochastic) gradient descent, and related topics into dynamical statements.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this chapter, we utilize dynamical systems to analyze several aspects of machine learning algorithms. As an expository contribution we demonstrate how to re-formulate a wide variety of challenges from deep neural networks, (stochastic) gradient descent, and related topics into dynamical statements. We also tackle three concrete challenges. First, we consider the process of information propagation through a neural network, i.e., we study the input-output map for different architectures. We explain the universal embedding property for augmented neural ODEs representing arbitrary functions of given regularity, the classification of multilayer perceptrons and neural ODEs in terms of suitable function classes, and the memory-dependence in neural delay equations. Second, we consider the training aspect of neural networks dynamically. We describe a dynamical systems perspective on gradient descent and study stability for overdetermined problems. We then extend this analysis to the overparameterized setting and describe the edge of stability phenomenon, also in the context of possible explanations for implicit bias. For stochastic gradient descent, we present stability results for the overparameterized setting via Lyapunov exponents of interpolation solutions. Third, we explain several results regarding mean-field limits of neural networks. We describe a result that extends existing techniques to heterogeneous neural networks involving graph limits via digraph measures. This shows how large classes of neural networks naturally fall within the framework of Kuramoto-type models on graphs and their large-graph limits. Finally, we point out that similar strategies to use dynamics to study explainable and reliable AI can also be applied to settings such as generative models or fundamental issues in gradient training methods, such as backpropagation or vanishing/exploding gradients.

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