Physics-Conditioned Video Diffusion with Kinematic Priors for Fusion Capsule Polishing

Video diffusion models can generate realistic process videos, but using them as controllable surrogates for industrial systems remains difficult when key control variables are only partially observable and no simulator or differentiable physics model is available. We study this regime in inertial confinement fusion (ICF) target capsule polishing, where surface defects can degrade fusion yield. The dynamics are governed by the polishing speed $\omega_p$ and capsule-pad slip $S_C$. While capsule motion can be monitored from video, $S_C$ is only indirectly observable. Using only an analytical kinematic model, we adapt a frozen Stable Video Diffusion (SVD-xt) backbone via LoRA and condition the generation on Fourier-encoded physics parameters $(\omega_p, S_C)$. Two training objectives operate on latent frame-difference dynamics: (i) a batch level correlation loss aligning them with the analytical sliding speed, and (ii) a stratified ratio-matching loss that, conditioned on $\omega_p$, isolates slip structure from the dominant $\omega_p$-driven variance. We evaluate physics consistency by tracking capsule motion in generated videos, inverting the kinematic model, and measuring rank agreement under a physical validity gate. For speed, rank accuracy exceeds $0.97$ on real data; for slip, it reaches $0.735$ with Spearman $\ge 0.50$ on synthetic data. Overall, the results suggest a practical recipe for controllable industrial video surrogates when only low-dimensional analytical kinematics are available.