Toward Goal-Agnostic Joint-Embedding Predictive Control of Partial Differential Equations
We present a goal-agnostic control framework for partial differential equations (PDEs) built around an end-to-end joint-embedding predictive architecture (JEPA). A lightweight 2D vision-transformer (ViT) and action-conditioned latent dynamics are trained offline without a reward or downstream goal, before being frozen and reused by a model-predictive path integral (MPPI) controller. We minimize a control objective in the latent space, initially expressed via the $L^2$ distance and additionally illustrate the benefit of recasting the control objective in terms of an explicit physical observable when available. By instead minimizing the tracking error for a learned linear kinetic-energy (KE...
| Authors | Jonathan Gallagher, Roberto Guglielmi |
|---|---|
| Primary category | cs.LG |
| Published | 21 July 2026 |
| Last updated at source | 4 August 2026 |
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