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SUMMARY:Fourier Neural Differential Equations for Learning Quantum Field T
 heories - Isaac Brant
DTSTART:20240524T120000Z
DTEND:20240524T130000Z
UID:TALK217159@talks.cam.ac.uk
CONTACT:Oisin Kim
DESCRIPTION:A Quantum Field Theory (QFT) is defined by its interaction Ham
 iltonian and linked to experimental data by the scattering matrix – a re
 lationship represented as a first order differential equation in time. Neu
 ral Differential Equations (NDEs) learn the time derivative of a residual 
 network’s hidden state and have proven efficacy in learning differential
  equations with physical constraints. To test the applicability of NDEs to
  QFTs\, NDE models are used to learn φ4 theory\, Scalar-Yukawa theory and
  Scalar Quantum Electrodynamics. A new NDE architecture is also introduced
 \, the Fourier Neural Differential Equation (FNDE)\, which combines NDE in
 tegration and Fourier network convolution. It is shown that by training on
  scattering data\, the interaction Hamiltonian of a theory can be extracte
 d from learnt network parameters.
LOCATION:SS03 - William Gates Building
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