Physics-informed neural networks solve two-flavor neutrino oscillation equations in vacuum and matter with mean squared errors of order 10^{-3} to 10^{-4}, matching analytical results.
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A conditional Wasserstein GAN generates complete kinematic event summaries for IBD-CC, NC, and NuEElastic electron neutrino interactions that match GENIE distributions in 1D marginals and correlations.
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Physics-Informed Neural Networks for Solving Two-Flavor Neutrino Oscillations in Vacuum and Matter Environments for Atmospheric and Reactor Neutrinos
Physics-informed neural networks solve two-flavor neutrino oscillation equations in vacuum and matter with mean squared errors of order 10^{-3} to 10^{-4}, matching analytical results.
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Conditional Wasserstein GAN for Simulating Neutrino Event Summaries using Incident Energy of Electron Neutrinos
A conditional Wasserstein GAN generates complete kinematic event summaries for IBD-CC, NC, and NuEElastic electron neutrino interactions that match GENIE distributions in 1D marginals and correlations.