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 Networks. 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.

翻译：计算效率与对抗鲁棒性是实际工程应用中的关键因素。然而，传统神经网络往往难以同时解决这两个问题，甚至无法单独有效应对。借鉴自然物理系统与现有研究的启示，已知输入凸性架构可提升计算效率，而Lipschitz约束架构能增强对抗鲁棒性。通过融合凸性与Lipschitz连续性的优势，我们提出了一种新型网络架构——输入凸性Lipschitz循环神经网络。该模型在计算效率与对抗鲁棒性方面，优于现有循环单元在多项工程任务中的表现。这些任务包括基准MNIST图像分类、新加坡LHT控股公司太阳能光伏系统规划中的实际太阳辐照度预测，以及化学反应器的实时模型预测控制优化。