# Physics-Guided Adversarial Machine Learning for Aircraft Systems Simulation

arXiv: https://arxiv.org/abs/2209.03431 · HTML: https://mlsanity.com/paper/2209.03431/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 39 references that are in the map.
- First posted on arXiv 7 Sep 2022.

## Builds on (most cited first, 16 of 16)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning](https://mlsanity.com/paper/1712.03141/) (2017)
- [Theory-guided Data Science: A New Paradigm for Scientific Discovery from Data](https://mlsanity.com/paper/1612.08544/) (2016)
- [GenAttack: Practical Black-box Attacks with Gradient-Free Optimization](https://mlsanity.com/paper/1805.11090/) (2018)
- [Label-Free Supervision of Neural Networks with Physics and Domain Knowledge](https://mlsanity.com/paper/1609.05566/) (2016)
- [A Survey of Black-Box Adversarial Attacks on Computer Vision Models](https://mlsanity.com/paper/1912.01667/) (2019)
- [SirenAttack: Generating Adversarial Audio for End-to-End Acoustic Systems](https://mlsanity.com/paper/1901.07846/) (2019)
- [Differentiable Physics-informed Graph Networks](https://mlsanity.com/paper/1902.02950/) (2019)
- [DeepEvolution: A Search-Based Testing Approach for Deep Neural Networks](https://mlsanity.com/paper/1909.02563/) (2019)
- [Adversarial Attacks, Regression, and Numerical Stability Regularization](https://mlsanity.com/paper/1812.02885/) (2018)
- [POBA-GA: Perturbation Optimized Black-Box Adversarial Attacks via Genetic Algorithm](https://mlsanity.com/paper/1906.03181/) (2019)
- [Towards Principled Design of Deep Convolutional Networks: Introducing SimpNet](https://mlsanity.com/paper/1802.06205/) (2018)
- [They Might NOT Be Giants: Crafting Black-Box Adversarial Examples with Fewer Queries Using Particle Swarm Optimization](https://mlsanity.com/paper/1909.07490/) (2019)
- [View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation](https://mlsanity.com/paper/1606.01552/) (2016)

## Built on by (most cited first, 1 of 1)

- [Machine Learning Robustness: A Primer](https://mlsanity.com/paper/2404.00897/) (2024)

## Abstract (arXiv)

In the context of aircraft system performance assessment, deep learning technologies allow to quickly infer models from experimental measurements, with less detailed system knowledge than usually required by physics-based modeling. However, this inexpensive model development also comes with new challenges regarding model trustworthiness. This work presents a novel approach, physics-guided adversarial machine learning (ML), that improves the confidence over the physics consistency of the model. The approach performs, first, a physics-guided adversarial testing phase to search for test inputs revealing behavioral system inconsistencies, while still falling within the range of foreseeable operational conditions. Then, it proceeds with physics-informed adversarial training to teach the model the system-related physics domain foreknowledge through iteratively reducing the unwanted output deviations on the previously-uncovered counterexamples. Empirical evaluation on two aircraft system performance models shows the effectiveness of our adversarial ML approach in exposing physical inconsistencies of both models and in improving their propensity to be consistent with physics domain knowledge.