One-Way Coupling via Patchwise Adaptive Normalization in Transformer Surrogates

Key points

• Many physical systems exhibit one-way coupling: exogenous fields such as medium density or obstacle geometry influence the dynamics without being affected by them.1
• We investigate how explicitly representing these fields can improve the accuracy and computational efficiency of transformer-based PDE surrogates.1
• We evaluate PAdaNorm on datasets from three different domains, using a Walrus-inspired transformer architecture.1
• An ablation over eight conditioning variants shows that modulating a single attention axis (temporal or spatial) suffices and that a lightweight shared MLP across blocks remains competitive with per-block projections.1

Sources (1)

• [1]One-Way Coupling via Patchwise Adaptive Normalization in Transformer Surrogates
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 10, 08:02 AM

  Building on spatially adaptive normalization, we introduce Patchwise Adaptive Normalization (PAdaNorm), which partitions exogenous fields into patches aligned with the state tokenization.

  Many physical systems exhibit one-way coupling: exogenous fields such as medium density or obstacle geometry influence the dynamics without being affected by them.

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