Akira Osaka

Self-Augmented Diffusion Guidance for Physics-Informed Generation

This study proposes a simple yet effective physics-informed approach based on diffusion guidance with self-generated data augmentation that significantly reduces the deviations compared with standard diffusion models but also achieves further reductions when combined with existing physics-constrained diffusion methods.