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Found 4 papers, 0 papers with code
Removing grid structure in angle-resolved photoemission spectra via deep learning method
Our method takes advantage of the self-correlation information within the spectra themselves and can greatly optimize the quality of the spectra while removing the grid structure and noise simultaneously.
Spectroscopic data de-noising via training-set-free deep learning method
De-noising plays a crucial role in the post-processing of spectra.
Learned Optimizers for Analytic Continuation
Traditional maximum entropy and sparsity-based algorithms for analytic continuation often suffer from the ill-posed kernel matrix or demand tremendous computation time for parameter tuning.
Recommender Engine for Continuous Time Quantum Monte Carlo Methods
Exploiting the quantum to classical mapping inherent in the continuous-time quantum Monte Carlo methods, we construct a classical molecular gas model to reproduce the quantum distributions.
