QuantifAI: Scalable Bayesian uncertainty quantification with data-driven (learned) priors

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QuantifAI is a PyTorch-based open-source radio interferometric imaging reconstruction package with scalable Bayesian uncertainty quantification relying on data-driven (learned) priors.

The methods developed and implemented in QuantifAI are also being ported to the C++ PURIFY and SOPT codes for exascale computing.

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Publications

Scalable Bayesian uncertainty quantification with data-driven priors for radio interferometric imaging

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Talks

Learned Exascale Computational Imaging (LEXCI): Update

Oct 2023 Bristol
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Scientific machine learning in astrophysics: machine learning for physics; physics for machine learning

Sep 2023 Harwell
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Learned Exascale Computational Imaging (LEXCI)

May 2023 University of Leicester
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