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Data-based modeling and control of nonlinear process systems using sparse identification: An overview of recent results
This paper discusses recent developments in the data-based modeling and control of
nonlinear chemical process systems using sparse identification of nonlinear dynamics …
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
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 …
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
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 …
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
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 …
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 …
biology approaches to analyze an intracellular signaling pathway. However, an accurate first …