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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

DOI retrieved: January 2, 2025

Additional Info

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