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.

翻译：本研究提出了知识蒸馏物理信息神经网络（KD-PINN），该框架通过连续调整Kullback-Leibler散度，将高容量教师模型的预测精度迁移至紧凑型学生模型。为验证其对不同动力学问题和维度的普适性，该框架在一组代表性偏微分方程（PDE）上进行了评估。在所有基准测试中，学生模型实现了从4.8倍（Navier-Stokes方程）到6.9倍（Burgers方程）的推理加速，同时保持了精度。当模型经过适当调优后，精度可提升约1%。蒸馏过程还表现出正则化效应。蒸馏模型在CPU上的平均推理延迟为5.3毫秒，达到了亚10毫秒性能定义的超低延迟实时运行标准。最后，本研究探讨了知识蒸馏如何降低PINNs的推理延迟，以推动高精度超低延迟神经偏微分方程求解器的发展。