Noise enhanced neural networks for analytic continuation

@article{Yao2022NoiseEN,
  title={Noise enhanced neural networks for analytic continuation},
  author={Juan Yao and Ce Wang and Zhi yuan Yao and Hui Zhai},
  journal={Machine Learning: Science and Technology},
  year={2022},
  volume={3}
}
  • Juan YaoCe Wang Hui Zhai
  • Published 24 November 2021
  • Computer Science
  • Machine Learning: Science and Technology
Analytic continuation maps imaginary-time Green’s functions obtained by various theoretical/numerical methods to real-time response functions that can be directly compared with experiments. Analytic continuation is an important bridge between many-body theories and experiments but is also a challenging problem because such mappings are ill-conditioned. In this work, we develop a neural network (NN)-based method for this problem. The training data is generated either using synthetic Gaussian… 

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