Physics-Informed Neural Networks (PINNs) For DVCS Cross Sections

Manal Almaeen, Jake Grigsby, Joshua Hoskins, Brandon Kriesten, Yaohang Li, Huey-Wen Lin, Simonetta Liuti & Sorawich Maichum
We present a physics informed deep learning technique for Deeply Virtual Compton Scattering (DVCS) cross sections from an unpolarized proton target using both an unpolarized and polarized electron beam. Training a deep learning model typically requires a large size of data that might not always be available or possible to obtain. Alternatively, a deep learning model can be trained using additional knowledge gained by enforcing some physics constraints such as angular symmetries for better accuracy...
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