Physics-Augmented Diffusion Modeling for satellite anomaly response operations with embodied agent feedback loops

Short summary

The author presents Physics-Augmented Diffusion Modeling (PADM), a framework that embeds orbital mechanics and spacecraft dynamics differential equations directly into a diffusion model's noise schedule and denoising process. An embodied reinforcement learning agent controls satellite actuators and feeds execution results back to the diffusion model, closing the loop between generation and physical execution. The article includes PyTorch code for a physics-embedded noise scheduler that preserves conservation laws like angular momentum.

• •PADM augments diffusion models with physics-informed constraints to ensure generated satellite anomaly responses are physically executable
• •Three components: physics-embedded noise schedule, constraint-guided denoising via differentiable physics solver, and embodied RL agent feedback loop
• •Includes PyTorch implementation of a noise scheduler that preserves angular momentum conservation across axes

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