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Machine learning and theoretical physics: some applications - Miranda Cheng
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Wednesday October 27, 2021
Speaker: Miranda Cheng (University of Amsterdam)
Title: Machine learning and theoretical physics: some applications
Abstract: In this talk I will briefly summarise my two recent papers on the interactions between physics and machine learning. In the paper with V Anagiannis, we exploit the analogy between quantum many-body systems and certain neural networks to analyse the learning process using quantum entanglement. In the second paper with de Haan, Rainone, and Bondesan, we use a continuous flow model to help ameliorate the numerical difficulties in sampling in lattice field theories, which for instance hampers high-precision computations in LQCD.
Speaker: Miranda Cheng (University of Amsterdam)
Title: Machine learning and theoretical physics: some applications
Abstract: In this talk I will briefly summarise my two recent papers on the interactions between physics and machine learning. In the paper with V Anagiannis, we exploit the analogy between quantum many-body systems and certain neural networks to analyse the learning process using quantum entanglement. In the second paper with de Haan, Rainone, and Bondesan, we use a continuous flow model to help ameliorate the numerical difficulties in sampling in lattice field theories, which for instance hampers high-precision computations in LQCD.
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