Data-based modeling and control of nonlinear process systems using sparse identification: An overview of recent results

F Abdullah, PD Christofides - Computers & Chemical Engineering, 2023 - Elsevier
This paper discusses recent developments in the data-based modeling and control of
nonlinear chemical process systems using sparse identification of nonlinear dynamics …

Hybrid modeling of first-principles and machine learning: A step-by-step tutorial review for practical implementation

P Shah, S Pahari, R Bhavsar, JSI Kwon - Computers & Chemical …, 2024 - Elsevier
In recent years, the integration of mechanistic process models with advanced machine
learning techniques has led to the development of hybrid models, which have shown …

SINDy-PI: a robust algorithm for parallel implicit sparse identification of nonlinear dynamics

K Kaheman, JN Kutz… - Proceedings of the …, 2020 - royalsocietypublishing.org
Accurately modelling the nonlinear dynamics of a system from measurement data is a
challenging yet vital topic. The sparse identification of nonlinear dynamics (SINDy) algorithm …

CrystalGPT: Enhancing system-to-system transferability in crystallization prediction and control using time-series-transformers

N Sitapure, JSI Kwon - Computers & Chemical Engineering, 2023 - Elsevier
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 …

Pysindy: a python package for the sparse identification of nonlinear dynamics from data

BM de Silva, K Champion, M Quade… - ar** an accurate first-principle model is an important step in employing systems
biology approaches to analyze an intracellular signaling pathway. However, an accurate first …