Point-Calibrated Spectral Neural Operators

ICLR 2025 Conference Submission1026 Authors

Keywords: Operator Learning, PDE

TL;DR: An innovative neural operator combining the point-level flexibility of attention mechanism and continuity prior of spectral-based methods.

Abstract: Two typical neural models have been extensively studied for operator learning, learning in spatial space via attention mechanism or learning in spectral space via spectral analysis technique such as Fourier Transform. Spatial learning enables point-level flexibility but lacks global continuity constraint, while spectral learning enforces spectral continuity prior but lacks point-wise adaptivity. This work innovatively combines the continuity prior and the point-level flexibility, with the introduced Point-Calibrated Spectral Transform. It achieves this by calibrating the preset spectral eigenfunctions with the predicted point-wise frequency preference via neural gate mechanism. Beyond this, we introduce Point-Calibrated Spectral Neural Operators, which learn operator mappings by approximating functions with the point-level adaptive spectral basis, thereby not only preserving the benefits of spectral prior but also boasting the superior adaptability comparable to the attention mechanis. Comprehensive experiments demonstrate its consistent performance enhancement in extensive PDE solving scenarios.

Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)

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Submission Number: 1026