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[ascl:2501.005] CIANNA: Convolutional Interactive Artificial Neural Networks by/for Astrophysicists

The CIANNA framework creates and trains deep-learning models for astronomical data analysis. Functionalities and optimizations are added based on relevance to astrophysical problem-solving. CIANNA builds and trains a wide variety of neural network architectures for various tasks through a high-level Python interface. It supports both computing on CPU and GPU acceleration through low-level CUDA programming, taking advantage of AI-dedicated hardware substructures. CIANNA distinguishes itself by its low latency, allowing tight integration with other codes.

Code site:
https://github.com/Deyht/CIANNA https://doi.org/10.5281/zenodo.12806324
Used in:
https://ui.adsabs.harvard.edu/abs/2024A%26A...690A.211C https://ui.adsabs.harvard.edu/abs/2023MNRAS.523.1967H https://ui.adsabs.harvard.edu/abs/2022arXiv220105571C
Bibcode:
2025ascl.soft01005C
Preferred citation method:

https://doi.org/10.5281/zenodo.12806324


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ascl:2501.005
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