Efficiently embedding and/or integrating mechanistic information with data-driven models is
essential if it is desired to simultaneously take advantage of both engineering principles and …

The thermochemical conversion of biomass is a promising technology due to its cost-
effectiveness and feedstock flexibility, with pyrolysis being a particularly noteworthy method …

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 …

Kinetic modeling of fermentation processes is difficult due to the use of micro-organisms that
follow complex reaction mechanisms. Kinetic models are usually not perfect owing to …

For prediction and real-time control tasks, machine-learning (ML)-based digital twins are
frequently employed. However, while these models are typically accurate, they are custom …

A hybrid model integrates a first‐principles model with a data‐driven model which predicts
certain unknown dynamics of the process, resulting in higher accuracy than first‐principles …

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 …

In this work, we present a physics-informed recurrent neural network (PIRNN)-based
modeling approach for nonlinear dynamic systems with parameter uncertainty. Physics …

During the multi-cycle operation of a Li-ion battery, its process dynamics evolve in two
distinct timescales: slow degradation dynamics over multiple cycles and fast cycling …