Physics-Augmented Diffusion Modeling for planetary geology survey missions for low-power autonomous deployments

Physics-Augmented Diffusion Modeling for planetary geology survey missions for low-power autonomous deployments A Personal Journey into Constrained AI My fascination with this problem began not in a cleanroom or a mission control center, but in my own backyard. I was experimenting with a small, solar-powered rover I'd built—a Raspberry Pi on wheels with a cheap camera. The goal was simple: autonomously identify and classify different types of rocks. I quickly hit a wall. The standard convolutional neural network I'd implemented was a power hog; the little rover's battery would drain in under an hour of active surveying. Furthermore, its predictions were often physically implausible—suggesting a porous, lightweight pumice stone could be resting at a 70-degree angle on a smooth surface, defying basic friction. This frustrating, hands-on experience sparked a deeper research question: How do we build geological AI that is both intelligent and efficient , and one that respects the fundament