RD-JEPA: Predictive latent pretraining for few-trajectory transfer across reaction--diffusion equations

arXiv:2609.29403 · cs.AI, cs.LG · Submitted 2026-09-24 · Read on arXiv

cs.AI, cs.LG

Submitted: 2026-09-24

Updated: 2026-09-24

Terminology

• Adaptation — 73× in this paper · explained in 1 episode
• Benchmark — 26× in this paper · explained in 2 episodes
• Aggregation — 16× in this paper · explained in 1 episode
• Transformer — 11× in this paper · explained in 2 episodes
• Embeddings — 7× in this paper · explained in 2 episodes
• Latent Representation — 6× in this paper · explained in 2 episodes
• Tokenization — 5× in this paper · explained in 2 episodes
• Foundation Model — 3× in this paper · explained in 3 episodes
• Meta-learning — 3× in this paper · explained in 2 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Foundation Models — 2× in this paper · explained in 7 episodes
• Inverse Problem — 2× in this paper · explained in 2 episodes
• Latent Representations — 2× in this paper · explained in 3 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• DeepONet — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Exponential Moving Average (EMA) — 1× in this paper · explained in 2 episodes
• In-Context Learning — 1× in this paper · explained in 4 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Model-Agnostic — 1× in this paper · explained in 2 episodes
• Self-Supervised Learning — 1× in this paper · explained in 8 episodes
• Symmetry Breaking — 1× in this paper · explained in 3 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes
• Uncertainty Quantification — 1× in this paper · explained in 8 episodes

Sources

• Representation Learning for Spatiotemporal Physical Systems
• PI-JEPA: Label-Free Surrogate Pretraining for Coupled Multiphysics Simulation via Operator-Split Latent Prediction

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