Artificial neural networks are used in various applications for data-driven black-box
modeling and subsequent optimization. Herein, we present an efficient method for …

An extensible open-source deterministic global optimizer (EAGO) programmed entirely in
the Julia language is presented. EAGO was developed to serve the need for supporting …

Several chemical engineering applications demand global optimization of nonconvex
process models, including safety verification and determination of thermodynamic equilibria …

In this work, we present general methods to construct convex/concave relaxations of the
activation functions that are commonly chosen for artificial neural networks (ANNs). The …

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 …

Convex relaxations of nonconvex functions provide useful bounding information in
applications such as deterministic global optimization and reachability analysis. In some …

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

Enclosing the reachable sets of nonlinear control systems is useful in state estimation and
safety verification for chemical process models. We present a new approach for computing …

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

Global optimization of processes through machine learning Page 1 Global Optimization of
Processes through Machine Learning Globale Prozessoptimierung durch maschinelles Lernen …