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Use active learning to determine the bounded-from-below region in parameter space of a multiscalar potential in quantum field theory.

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BFBrain

Installation | Documentation/Tutorial | Examples

Use active learning to determine the bounded-from-below region in parameter space of a multiscalar potential in quantum field theory.

Installation

Note that it is highly recommended that BFBrain only be run on a machine with Tensorflow and Jax both installed with full GPU support. Once these installations are completed, install BFBrain with the command:

$ pip install bfbrain

License

BFBrain was created by George Wojcik. It is licensed under the terms of the MIT license.

Cite us

If BFBrain has proved useful in your research, please give thought to citing our original paper which introduces the BFBrain package and describes its analysis approach. To cite it, use:

@article{Wojcik:2023usm,
    author = "Wojcik, George N.",
    title = "{BFBrain: Scalar Bounded-From-Below Conditions from Bayesian Active Learning}",
    eprint = "2309.10959",
    archivePrefix = "arXiv",
    primaryClass = "hep-ph",
    month = "9",
    year = "2023"
}

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Use active learning to determine the bounded-from-below region in parameter space of a multiscalar potential in quantum field theory.

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