Physics-Augmented Diffusion Modeling for planetary geology survey missions across multilingual stakeholder groups
Introduction: My Learning Journey into Physics-Augmented Diffusion
It was late one evening, deep into a research rabbit hole, when I stumbled across a paper that fundamentally changed how I think about generative modeling for scientific applications. I had been working on diffusion models for months—training them to generate synthetic geological maps of Martian terrain, experimenting with latent space interpolations, and pushing the boundaries of what these models could do for planetary science. But something was missing. The generated samples looked plausible, yet they consistently violated basic physical laws. Craters appeared where gravity would have long since erased them. Erosion patterns formed in impossible configurations. The models were learning statistical correlations, but not the underlying physics.
That night, while reading about Hamiltonian mechanics and its applications in machine learning, I had an epiphany: what if we could bake physical constraints directly into the diffusion process? Not as a post-processing step, but as an integral part of the forward and reverse diffusion dynamics. This realization sent me on a six-month exploration that combined my background in AI automation with deep dives into computational physics, multilingual NLP systems, and agentic AI architectures.
