It is well-documented how artificial intelligence can have (and already is having) a big
impact on chemical engineering. But classical machine learning approaches may be weak …

The optimization and machine learning toolkit (OMLT) is an open-source software package
incorporating neural network and gradient-boosted tree surrogate models, which have been …

Recent developments in data science and machine learning have inspired a new wave of
research into data-driven modeling for mathematical optimization of process applications …

Future projections of municipal solid waste (MSW) generation trends can resolve data
inadequacy in formulating a sustainable MSW management framework. Artificial neural …

Most optimization problems in engineering can be formulated as 'expensive'black box
problems whose solutions are limited by the number of function evaluations. Frequently …

Bayesian Optimization is a useful tool for experiment design. Unfortunately, the classical,
sequential setting of Bayesian Optimization does not translate well into laboratory …

Abstract Machine learning models are promising as surrogates in optimization when
replacing difficult to solve equations or black-box type models. This work demonstrates the …

Tree ensembles can be well-suited for black-box optimization tasks such as algorithm tuning
and neural architecture search, as they achieve good predictive performance with little or no …

Energy systems optimization problems are complex due to strongly non-linear system
behavior and multiple competing objectives, eg economic gain vs. environmental impact …

Optimization over trained machine learning models has applications including: verification,
minimizing neural acquisition functions, and integrating a trained surrogate into a larger …