论文标题
评估傅立叶神经操作员的对抗性鲁棒性
Evaluating the Adversarial Robustness for Fourier Neural Operators
论文作者
论文摘要
近年来，机器学习（ML）驱动的方法已被广泛用于科学发现域。其中，傅立叶神经操作员（FNO）是第一个以零拍超分辨率和卓越精度模拟湍流的模拟流量，与传统的偏微分方程（PDE）求解器相比，这可以显着提高速度。为了检查可信度，我们通过基于规范的数据输入扰动来生成对FNO的对抗性示例，从而提供了有关科学发现模型对抗性鲁棒性的首次研究。我们的结果对FNO模型的输出和PDE求解器的输出之间的平均平方误差进行了评估，我们的结果表明，随着摄动水平的增加，该模型的稳健性迅速降解，尤其是在2D Darcy和Navier Case等非示例案例中。我们的研究提供了一种敏感性分析工具和评估原则，用于评估基于ML的科学发现模型的对抗性鲁棒性。
In recent years, Machine-Learning (ML)-driven approaches have been widely used in scientific discovery domains. Among them, the Fourier Neural Operator (FNO) was the first to simulate turbulent flow with zero-shot super-resolution and superior accuracy, which significantly improves the speed when compared to traditional partial differential equation (PDE) solvers. To inspect the trustworthiness, we provide the first study on the adversarial robustness of scientific discovery models by generating adversarial examples for FNO, based on norm-bounded data input perturbations. Evaluated on the mean squared error between the FNO model's output and the PDE solver's output, our results show that the model's robustness degrades rapidly with increasing perturbation levels, particularly in non-simplistic cases like the 2D Darcy and the Navier cases. Our research provides a sensitivity analysis tool and evaluation principles for assessing the adversarial robustness of ML-based scientific discovery models.
