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- W2982172145 abstract "A method for machine learning and serving of discrete field theories in physics is developed. The learning algorithm trains a discrete field theory from a set of observed data on a spacetime lattice, and the serving algorithm uses the learned discrete field theory to predict new observations of the field for new boundary and initial conditions. The approach to learn discrete field theories overcomes the difficulties associated with learning continuous theories by artificial intelligence. The serving algorithm of discrete field theories belongs to the family of structure-preserving geometric algorithms, which have been proven to be superior than the conventional algorithms based on discretization of differential equations. The effectiveness of the method and algorithms developed is demonstrated using the examples of learning and predicting nonlinear oscillations. The illustrated advantages of discrete field theories relative to continuous theories in terms of machine learning compatibility are consistent with Bostrom's simulation hypothesis." @default.
- W2982172145 created "2019-11-01" @default.
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- W2982172145 date "2019-10-22" @default.
- W2982172145 modified "2023-09-27" @default.
- W2982172145 title "Machine learning and serving of discrete field theories" @default.
- W2982172145 hasPublicationYear "2019" @default.
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