Data Free Training of Diffusion Models for Lattice Field Theories

The paper addresses the computational challenge of sampling equilibrium field configurations in lattice field theory by proposing a data‑free training method for diffusion models. Instead of relying on target distribution samples, the authors train the models using their own generated samples and the target score identity, leveraging importance‑weighted and self‑consistent fixed‑point formulations. Experiments on two‑dimensional φ⁴ theory demonstrate that this approach achieves performance comparable to training with HMC reference data across various phases.

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• diffusion
• benchmarks