Modeling and Control of Nonlinear Processes Using Sparse Identification: Using Dropout to Handle Noisy Data

Process structure-based recurrent neural network modeling for predictive control: A comparative study

(2022) Mohammed S. Alhajeri et al.   CHEMICAL ENGINEERING RESEARCH & DESIGN

A tutorial review of neural network modeling approaches for model predictive control

(2022) Yi Ming Ren et al.   COMPUTERS & CHEMICAL ENGINEERING

Physics-informed machine learning modeling for predictive control using noisy data

(2022) Mohammed S. Alhajeri et al.   CHEMICAL ENGINEERING RESEARCH & DESIGN

Machine learning modeling and predictive control of nonlinear processes using noisy data

(2021) Zhe Wu et al.   AICHE JOURNAL

Machine learning-based predictive control using noisy data: evaluating performance and robustness via a large-scale process simulator

(2021) Zhe Wu et al.   CHEMICAL ENGINEERING RESEARCH & DESIGN

Sparse identification of nonlinear dynamical systems via reweighted ℓ1-regularized least squares

(2021) Alexandre Cortiella et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Nonlinear dynamical system identification using the sparse regression and separable least squares methods

(2021) Miaomiao Lin et al.   JOURNAL OF SOUND AND VIBRATION

Handling noisy data in sparse model identification using subsampling and co-teaching

(2021) Fahim Abdullah et al.   COMPUTERS & CHEMICAL ENGINEERING

Sparse-identification-based model predictive control of nonlinear two-time-scale processes

(2021) Fahim Abdullah et al.   COMPUTERS & CHEMICAL ENGINEERING

How entropic regression beats the outliers problem in nonlinear system identification

(2020) Abd AlRahman R. AlMomani et al.   CHAOS

Feature engineering in big data analytics for IoT-enabled smart manufacturing – Comparison between deep learning and statistical learning

(2020) Devarshi Shah et al.   COMPUTERS & CHEMICAL ENGINEERING

Data-based reduced-order modeling of nonlinear two-time-scale processes

(2020) Fahim Abdullah et al.   CHEMICAL ENGINEERING RESEARCH & DESIGN

SubTSBR to tackle high noise and outliers for data-driven discovery of differential equations

(2020) Sheng Zhang et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Model selection for hybrid dynamical systems via sparse regression

(2019) N. M. Mangan et al.   PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES

Closed-loop identification and model predictive control of extractive dividing-wall column

(2019) Zemin Feng et al.   CHEMICAL ENGINEERING AND PROCESSING-PROCESS INTENSIFICATION

Sparse identification of nonlinear dynamics for rapid model recovery

(2018) Markus Quade et al.   CHAOS

Sparse learning of stochastic dynamical equations

(2018) Lorenzo Boninsegna et al.   JOURNAL OF CHEMICAL PHYSICS

Constrained sparse Galerkin regression

(2018) Jean-Christophe Loiseau et al.   JOURNAL OF FLUID MECHANICS

Optimization and Control of a Thin Film Growth Process: A Hybrid First Principles/Artificial Neural Network Based Multiscale Modelling Approach

(2018) Donovan Chaffart et al.   COMPUTERS & CHEMICAL ENGINEERING

Utilizing big data for batch process modeling and control

(2018) Abhinav Garg et al.   COMPUTERS & CHEMICAL ENGINEERING

Deep learning algorithm for data-driven simulation of noisy dynamical system

(2018) Kyongmin Yeo et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Sparse identification of nonlinear dynamics for model predictive control in the low-data limit

(2018) E. Kaiser et al.   PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES

Exact Recovery of Chaotic Systems from Highly Corrupted Data

(2017) Giang Tran et al.   MULTISCALE MODELING & SIMULATION

Sparse identification of a predator-prey system from simulation data of a convection model

(2017) Magnus Dam et al.   PHYSICS OF PLASMAS

Learning partial differential equations via data discovery and sparse optimization

(2017) Hayden Schaeffer   PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES

Discovering governing equations from data by sparse identification of nonlinear dynamical systems

(2016) Steven L. Brunton et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Low-Dimensional Approach for Reconstruction of Airfoil Data via Compressive Sensing

(2015) Zhe Bai et al.   AIAA JOURNAL

A New Subspace Identification Approach Based on Principal Component Analysis and Noise Estimation

(2015) Ping Wu et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

Data-driven model reduction-based nonlinear MPC for large-scale distributed parameter systems

(2015) Weiguo Xie et al.   JOURNAL OF PROCESS CONTROL

Exploiting sparsity and equation-free architectures in complex systems

(2014) J. L. Proctor et al.   European Physical Journal-Special Topics

On the Compressive Spectral Method

(2014) Alan Mackey et al.   MULTISCALE MODELING & SIMULATION

Compressive Sensing and Low-Rank Libraries for Classification of Bifurcation Regimes in Nonlinear Dynamical Systems

(2014) Steven L. Brunton et al.   SIAM JOURNAL ON APPLIED DYNAMICAL SYSTEMS

Data-driven model predictive quality control of batch processes

(2013) Siam Aumi et al.   AICHE JOURNAL

Integration of process design and control: A review

(2013) Mahdi Sharifzadeh   CHEMICAL ENGINEERING RESEARCH & DESIGN

Real-Time Optimization of an Industrial-Scale Vapor Recompression Distillation Process. Model Validation and Analysis

(2013) Diego. F. Mendoza et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

Sparse dynamics for partial differential equations

(2013) H. Schaeffer et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Compressed modes for variational problems in mathematics and physics

(2013) V. Ozolins et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Ensemble approaches for regression

(2012) João Mendes-Moreira et al.   ACM COMPUTING SURVEYS

Nonlinear Bayesian state estimation: A review of recent developments

(2012) Sachin C. Patwardhan et al.   CONTROL ENGINEERING PRACTICE

Tuning SISO offset-free Model Predictive Control based on ARX models

(2012) Jakob Kjøbsted Huusom et al.   JOURNAL OF PROCESS CONTROL

Economic model predictive control of nonlinear process systems using Lyapunov techniques

(2011) Mohsen Heidarinejad et al.   AICHE JOURNAL

Predicting Catastrophes in Nonlinear Dynamical Systems by Compressive Sensing

(2011) Wen-Xu Wang et al.   PHYSICAL REVIEW LETTERS

Data-driven predictive control for blast furnace ironmaking process

(2010) Jiu-sun Zeng et al.   COMPUTERS & CHEMICAL ENGINEERING

Identification of ARX and ARARX Models in the Presence of Input and Output Noises

(2010) Roberto Diversi et al.   EUROPEAN JOURNAL OF CONTROL

Nonlinear system identification for predictive control using continuous time recurrent neural networks and automatic differentiation

(2008) R.K. Al Seyab et al.   JOURNAL OF PROCESS CONTROL

Long-term time series prediction with the NARX network: An empirical evaluation

(2008) José Maria P. Menezes et al.   NEUROCOMPUTING

Nonlinear model predictive control for distributed parameter systems using data driven artificial neural network models

(2007) Eleni Aggelogiannaki et al.   COMPUTERS & CHEMICAL ENGINEERING