# 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} }

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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