Computational efficiency and adversarial robustness are critical factors in real-world engineering applications. Yet, conventional neural networks often fall short in addressing both simultaneously, or even separately. Drawing insights from natural physical systems and existing literature, it is known that an input convex architecture enhances computational efficiency, while a Lipschitz-constrained architecture bolsters adversarial robustness. By leveraging the strengths of convexity and Lipschitz continuity, we develop a novel network architecture, termed Input Convex Lipschitz Recurrent Neural Network. This model outperforms existing recurrent units across a spectrum of engineering tasks in terms of computational efficiency and adversarial robustness. These tasks encompass a benchmark MNIST image classification, real-world solar irradiance prediction for Solar PV system planning at LHT Holdings in Singapore, and real-time Model Predictive Control optimization for a chemical reactor.

通过结合凸特性和Lipschitz连续性，开发了一种名为Input Convex Lipschitz Recurrent Neural Network的新型网络架构，该模型在计算效率和对抗鲁棒性方面胜过现有的循环单元，适用于多种工程任务，包括图像分类、太阳能预测和实时模型预测控制优化。

输入凸型Lipschitz循环神经网络：一种高效稳健的工程任务方法