Out-of-distribution generalization of deep-learning surrogates for 2D PDE-generated dynamics in the small-data regime

• URL: http://arxiv.org/abs/2601.08404v1
• Date: Tue, 13 Jan 2026 10:20:59 GMT
• Title: Out-of-distribution generalization of deep-learning surrogates for 2D PDE-generated dynamics in the small-data regime
• Authors: Binh Duong Nguyen, Stefan Sandfeld,
• Abstract summary: We study autoregressive deep-learning surrogates for two-dimensional PDE dynamics on periodic domains.<n>In small-data periodic 2D PDE settings, convolutional architectures with inductive biases aligned to locality remain strong contenders for accurate and moderately out-of-distribution-robust surrogate modeling.
• Score: 1.9116784879310027
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Partial differential equations (PDEs) are a central tool for modeling the dynamics of physical, engineering, and materials systems, but high-fidelity simulations are often computationally expensive. At the same time, many scientific applications can be viewed as the evolution of spatially distributed fields, making data-driven forecasting of such fields a core task in scientific machine learning. In this work we study autoregressive deep-learning surrogates for two-dimensional PDE dynamics on periodic domains, focusing on generalization to out-of-distribution initial conditions within a fixed PDE and parameter regime and on strict small-data settings with at most $\mathcal{O}(10^2)$ simulated trajectories per system. We introduce a multi-channel U-Net [...], evaluate it on five qualitatively different PDE families and compare it to ViT, AFNO, PDE-Transformer, and KAN-UNet under a common training setup. Across all datasets, me-UNet matches or outperforms these more complex architectures in terms of field-space error, spectral similarity, and physics-based metrics for in-distribution rollouts, while requiring substantially less training time. It also generalizes qualitatively to unseen initial conditions with as few as $\approx 20$ training simulations. A data-efficiency study and Grad-CAM analysis further suggest that, in small-data periodic 2D PDE settings, convolutional architectures with inductive biases aligned to locality and periodic boundary conditions remain strong contenders for accurate and moderately out-of-distribution-robust surrogate modeling.

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