Interaction Field Matching: Overcoming Limitations of Electrostatic Models
Journal:
arXiv
Published Date:
Jun 3, 2025
Abstract
Electrostatic field matching (EFM) has recently appeared as a novel
physics-inspired paradigm for data generation and transfer using the idea of an
electric capacitor. However, it requires modeling electrostatic fields using
neural networks, which is non-trivial because of the necessity to take into
account the complex field outside the capacitor plates. In this paper, we
propose Interaction Field Matching (IFM), a generalization of EFM which allows
using general interaction fields beyond the electrostatic one. Furthermore,
inspired by strong interactions between quarks and antiquarks in physics, we
design a particular interaction field realization which solves the problems
which arise when modeling electrostatic fields in EFM. We show the performance
on a series of toy and image data transfer problems.
