Title:Toward Goal-Agnostic Joint-Embedding Predictive Control of Partial Differential Equations

Abstract:We present a goal-agnostic control framework for partial differential equations (PDEs) built around a joint-embedding predictive architecture (JEPA). The small 2D ViT encoder and action-conditioned latent dynamics are trained offline without a reward or downstream goal, frozen, and reused by a model-predictive path integral (MPPI) controller. We find that when available, the control objective is better applied to an explicit physical observable (provided injectivity) than to minimizing raw Euclidean distance ($L^2$) in the learned latent space. For a learned linear kinetic-energy (KE) probe on frozen latent rollouts we can reproduce held-out trajectories with $R^2=0.989$, while requiring no change to the underlying world model. On the PDE Control Gym 2D Navier--Stokes benchmark, using KE-probe planning improves the matched 50-episode native reward from $-12.08\pm0.86$ for latent-$L^2$ planning to $-10.90\pm0.91$ (95\% CI), while lowering last-quarter velocity-field RMSE from $0.0765$ to $0.0692$. Across three intentionally withheld, dissimilar, aperiodic targets, KE planning lowers late field RMSE by $53\%$ relative to latent-$L^2$ planning ($0.0220$ versus $0.0469$), winning all 30 paired episodes. The same frozen model also supports controls targeting stabilization around a steady configuration via direct regulation of KE achieving $2.7\%$ mean relative error. While the latent probe is brittle to measurement noise and missing pixels, we believe the results support the claim that latent dynamics can remain both dynamic and goal-agnostic while calibrated observables (granted they guarantee unique continuation) may be a better objective for state control

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