Evaluating the Adversarial Robustness for Fourier Neural Operators
- URL: http://arxiv.org/abs/2204.04259v1
- Date: Fri, 8 Apr 2022 19:19:42 GMT
- Title: Evaluating the Adversarial Robustness for Fourier Neural Operators
- Authors: Abolaji D. Adesoji and Pin-Yu Chen
- Abstract summary: Fourier Neural Operator (FNO) was the first to simulate turbulent flow with zero-shot super-resolution.
We generate adversarial examples for FNO based on norm-bounded data input perturbations.
Our results show that the model's robustness degrades rapidly with increasing perturbation levels.
- Score: 78.36413169647408
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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