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Neural Abstractions for Dynamical Models

The dataset used in this paper is a collection of neural abstractions for dynamical models. The dataset consists of neural networks with different activation functions (piecewise constant, piecewise affine, and sigmoidal) and different architectures. The dataset is used to evaluate the trade-off between efficiency and precision of neural abstractions.

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Cite this as

Alec Edwards, Mirco Giacobbe, Alessandro Abate (2025). Dataset: Neural Abstractions for Dynamical Models. https://doi.org/10.57702/n72vr19l

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Additional Info

Field	Value
Created	January 2, 2025
Last update	January 2, 2025
Defined In	https://doi.org/10.48550/arXiv.2307.15546
Author	Alec Edwards
More Authors	Mirco Giacobbe Alessandro Abate