The last two years have seen groundbreaking advances in natural language processing
(NLP) with the advent of applications like ChatGPT, Codex, and ChatSonic. This revolution …

This work develops a framework for building machine learning models and machine-
learning-based predictive control schemes for batch crystallization processes. We consider …

Given the hesitance surrounding the direct implementation of black-box tools due to safety
and operational concerns, fully data-driven deep-neural-network (DNN)-based digital twins …

This work presents a framework for developing physics-informed recurrent neural network
(PIRNN) models and PIRNN-based predictive control schemes for batch crystallization …

The porosity, order, biocompatibility, and chirality of protein crystals has motivated interest
from diverse research domains including materials science, biotechnology, and medicine …

Recently, thin-film deposition of quantum dot (QDs) to manufacture solar cells and displays
have received significant attention due to the lucrative optoelectronic properties of these …

Crystal morphology is known to be of great importance to the end-use properties of crystal
products, and to affect down-stream processing such as filtration and drying. However, it has …

The purpose of this study was to employ Artificial Neural Networks (ANNs) to develop data-
driven models that would enable the shrinking horizon nonlinear model predictive control of …

Neural networks (NNs) have emerged as a state-of-the-art method for modeling nonlinear
systems in model predictive control (MPC). However, the robustness of NNs, in terms of …

The self-assembly process, where molecules form complex structures through interaction
forces, has broad applications in various fields. However, controlling the dynamics of self …