Hybrid modeling, with an appropriate blend of the mechanistic and data‐driven framework,
is increasingly being adopted in bioprocess modeling, model‐based experimental design …

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 …

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 …

The growing demand for various types of paper highlights the importance of optimizing the
kraft pulping process to achieve desired paper properties. This work proposes a novel …

Deep learning is emerging in many industrial sectors in hand with big data analytics to
streamline production. In the biomanufacturing sector, big data infrastructure is lagging …

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 …

Recently, lignin has garnered significant research attention due to its abundance in nature.
However, lignin is viewed as a recalcitrance factor as it impedes the overall biomass …

Hybrid modeling has gained substantial recognition due to its capacity to seamlessly
integrate machine learning methodologies while preserving the fundamental physical …