  Computational efficiency and non-adversarial robustness are critical factors
in process modeling and optimization for 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 theoretically that an input convex
architecture will enhance computational efficiency, while a
Lipschitz-constrained architecture will bolster non-adversarial robustness.
However, integrating both properties into one model is a nontrivial task, as
enforcing one property may compromise the other one. Therefore, in this work,
we develop a novel network architecture, termed Input Convex Lipschitz
Recurrent Neural Networks, that inherits the strengths of both convexity and
Lipschitz continuity. This model is explicitly designed for fast and robust
optimization-based tasks, which outperforms existing recurrent units in terms
of computational efficiency and non-adversarial robustness. Additionally, we
have successfully implemented this model in various practical engineering
applications, such as optimization of chemical processes and real-world solar
irradiance prediction for Solar PV system planning at LHT Holdings in
Singapore. Source code is available at
https://github.com/killingbear999/ICLRNN.
