In the past decade, deep learning became the prevalent methodology for predictive
modeling thanks to the remarkable accuracy of deep neural networks in tasks such as …

Reinforcement learning (RL) is a promising solution for difficult decision-making problems,
such as inventory management in chemical supply chains. However, enabling RL to …

With increasing digitalization and vertical integration of chemical process systems,
nonconvex optimization problems often emerge in chemical engineering applications, yet …

The robust design of performance/safety-critical process systems, from a model-based
perspective, remains an existing challenge. Hybrid first-principles data-driven models offer …

The non-convex nature of trained neural networks has created significant obstacles in their
incorporation into optimization models. Considering the wide array of applications that this …

Many real-world expensive industrial and engineering multi-objective optimization problems
(MOPs) are driven by historical, experimental, or simulation data. In such scenarios, due to …

Deterministic nonconvex optimization solvers generate convex relaxations of nonconvex
functions by making use of underlying factorable representations. One approach introduces …

In the past few years, rapid strides have been made in the modelling of complex systems
due to the advent of machine learning (ML) technologies. In particular, the transformer …

Following the discovery of the least squares method in 1805 by Legendre and later in 1809
by Gauss, surrogate modeling and machine learning have come a long way. From …

Engineered systems exhibit transient spatially dependent phenomena. Advances in
computational methods enable applications of quantitative and formal methods for …