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arXiv · 2512.13336

KD-PINN: Knowledge-Distilled PINNs for ultra-low-latency real-time neural PDE solvers

Karim Bounja·Lahcen Laayouni·Abdeljalil Sakat
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Abstract

This work introduces Knowledge-Distilled Physics-Informed Neural Networks (KD-PINN), a framework that transfers the predictive accuracy of a high-capacity teacher model to a compact student through a continuous adaptation of the Kullback-Leibler divergence. In order to confirm its generality for various dynamics and dimensionalities, the framework is evaluated on a representative set of partial differential equations (PDEs). Across the considered benchmarks, the student model achieves inference speedups ranging from x4.8 (Navier-Stokes) to x6.9 (Burgers), while preserving accuracy. Accuracy is improved by on the order of 1% when the model is properly tuned. The distillation process also revealed a regularizing effect. With an average inference latency of 5.3 ms on CPU, the distilled models enter the ultra-low-latency real-time regime defined by sub-10 ms performance. Finally, this study examines how knowledge distillation reduces inference latency in PINNs, to contribute to the development of accurate ultra-low-latency neural PDE solvers.

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Karim Bounja, Lahcen Laayouni, Abdeljalil Sakat. 2025-12-15. KD-PINN: Knowledge-Distilled PINNs for ultra-low-latency real-time neural PDE solvers. https://arxiv.org/abs/2512.13336

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