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

arXiv cs.LG5d4 min read

arXiv:2607.21644v1 Announce Type: new 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. F

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

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