https://doi.org/10.26089/NumMet.v27r436
Electric currents for discrete data generation
Keywords:
discrete data generation
electric currents
probability flow
distribution transfer
Abstract
In this paper, we propose a new method for data generation in discrete settings called Electric Current Discrete Data Generation (ECD2G). The method establishes an analogy between electric current flow in a circuit and the transfer of probability mass between data distributions. Samples from the source distribution are treated as current input nodes of a circuit, while samples from the target distribution are interpreted as current output nodes. A neural network is used to learn the distribution of electric currents describing the probability flows in the circuit. To map the source distribution to the target one, source samples are transferred through the circuit pathways according to the learned currents. This process ensures the transfer between data distributions without loss of probability in the exact circuit construction. The results of numerical computational experiments are presented to illustrate applicability and effectiveness of the proposed ECD2G method.
Published
2026-10-05
Issue
Section
Methods and algorithms of computational mathematics and their applications
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Copyright (c) 2026 А. С. Колесов, С. И. Манухов, В. В. Палюлин, А. А. Коротин
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