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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Deep hybrid model‐based predictive control with guarantees on domain of applicability
Mohammed Saad Faizan Bangi, Joseph Sang‐Il Kwon
AIChE Journal (2022) Vol. 69, Iss. 5
Closed Access | Times Cited: 38

Showing 1-25 of 38 citing articles:

Exploring the potential of time-series transformers for process modeling and control in chemical systems: An inevitable paradigm shift?
Niranjan Sitapure, Joseph Sang‐Il Kwon
Process Safety and Environmental Protection (2023) Vol. 194, pp. 461-477
Closed Access | Times Cited: 78
CrystalGPT: Enhancing system-to-system transferability in crystallization prediction and control using time-series-transformers
Niranjan Sitapure, Joseph Sang‐Il Kwon
Computers & Chemical Engineering (2023) Vol. 177, pp. 108339-108339
Open Access | Times Cited: 52
Introducing Hybrid Modeling with Time-Series-Transformers: A Comparative Study of Series and Parallel Approach in Batch Crystallization
Niranjan Sitapure, Joseph Sang‐Il Kwon
Industrial & Engineering Chemistry Research (2023) Vol. 62, Iss. 49, pp. 21278-21291
Open Access | Times Cited: 40
Unveiling Latent Chemical Mechanisms: Hybrid Modeling for Estimating Spatiotemporally Varying Parameters in Moving Boundary Problems
Silabrata Pahari, Parth Shah, Joseph Sang‐Il Kwon
Industrial & Engineering Chemistry Research (2024) Vol. 63, Iss. 3, pp. 1501-1514
Closed Access | Times Cited: 16
Physics-informed machine learning for MPC: Application to a batch crystallization process
Guoquan Wu, Wallace Tan Gian Yion, Khoa Le Nguyen Quang Dang, et al.
Process Safety and Environmental Protection (2023) Vol. 192, pp. 556-569
Open Access | Times Cited: 35
Hybrid modeling in bioprocess dynamics: Structural variabilities, implementation strategies, and practical challenges
Biswanath Mahanty
Biotechnology and Bioengineering (2023) Vol. 120, Iss. 8, pp. 2072-2091
Open Access | Times Cited: 23
Achieving robustness in hybrid models: A physics-informed regularization approach for spatiotemporal parameter estimation in PDEs
Silabrata Pahari, Parth Shah, Joseph Sang‐Il Kwon
Process Safety and Environmental Protection (2024) Vol. 204, pp. 292-302
Closed Access | Times Cited: 11
Machine learning meets process control: Unveiling the potential of LSTMc
Niranjan Sitapure, Joseph Sang‐Il Kwon
AIChE Journal (2024) Vol. 70, Iss. 7
Open Access | Times Cited: 9
Computational applications using data driven modeling in process Systems: A review
Sumit K. Bishnu, Sabla Y. Alnouri, Dhabia M. Al-Mohannadi
Digital Chemical Engineering (2023) Vol. 8, pp. 100111-100111
Open Access | Times Cited: 18
A review and perspective on hybrid modeling methodologies
Artur M. Schweidtmann, Dongda Zhang, Moritz von Stosch
Digital Chemical Engineering (2023) Vol. 10, pp. 100136-100136
Open Access | Times Cited: 17
Modeling and Control of a Chemical Process Network Using Physics-Informed Transfer Learning
Ming Xiao, Zhe Wu
Industrial & Engineering Chemistry Research (2023) Vol. 62, Iss. 42, pp. 17216-17227
Closed Access | Times Cited: 16
Hybrid modeling of first-principles and machine learning: A step-by-step tutorial review for practical implementation
Parth Shah, Silabrata Pahari, Raj Bhavsar, et al.
Computers & Chemical Engineering (2024), pp. 108926-108926
Closed Access | Times Cited: 5
Design and Analysis of a New COVID-19 Model with Comparative Study of Control Strategies
Azhar Iqbal Kashif Butt, Saira Batool, Muhammad Imran, et al.
Mathematics (2023) Vol. 11, Iss. 9, pp. 1978-1978
Open Access | Times Cited: 13
Hybrid deep modeling of a CHO-K1 fed-batch process: combining first-principles with deep neural networks
José M. Pinto, João R. C. Ramos, Rafael S. Costa, et al.
Frontiers in Bioengineering and Biotechnology (2023) Vol. 11
Open Access | Times Cited: 12
Hybrid dynamic modeling of an industrial reactor network with first-principles and data-driven approaches
Changrui Xie, Runjie Yao, Lingyu Zhu, et al.
Chemical Engineering Science (2024) Vol. 289, pp. 119852-119852
Closed Access | Times Cited: 3
A data-driven framework integrating Lyapunov-based MPC and OASIS-based observer for control beyond training domains
Bhavana Bhadriraju, Joseph Sang‐Il Kwon, Faisal Khan
Journal of Process Control (2024) Vol. 138, pp. 103224-103224
Closed Access | Times Cited: 3
Practical data-driven modeling and robust predictive control of mammalian cell fed-batch process
Laurent Dewasme, Meeri Mäkinen, Véronique Chotteau
Computers & Chemical Engineering (2023) Vol. 171, pp. 108164-108164
Closed Access | Times Cited: 9
Koopman Operator-Based Integrated Guidance and Control for Strap-Down High-Speed Missiles
Min Zhou, Mingfei Lu, Guanjie Hu, et al.
IEEE Transactions on Control Systems Technology (2024) Vol. 32, Iss. 6, pp. 2436-2443
Closed Access | Times Cited: 2
Simultaneous digital twin identification and signal-noise decomposition through modified generalized sparse identification of nonlinear dynamics
Jingyi Wang, Jesús Moreira, Yankai Cao, et al.
Computers & Chemical Engineering (2023) Vol. 177, pp. 108294-108294
Open Access | Times Cited: 7
From Shallow to Deep Bioprocess Hybrid Modeling: Advances and Future Perspectives
Roshanak Agharafeie, João R. C. Ramos, Jorge M. Mendes, et al.
Fermentation (2023) Vol. 9, Iss. 10, pp. 922-922
Open Access | Times Cited: 7
Physics-informed neural networks for state reconstruction of hydrogen energy transportation systems
Lu Zhang, Junyao Xie, Qingqing Xu, et al.
Computers & Chemical Engineering (2024) Vol. 192, pp. 108898-108898
Closed Access | Times Cited: 2
Control Lyapunov‐barrier function‐based safe reinforcement learning for nonlinear optimal control
Yujia Wang, Zhe Wu
AIChE Journal (2023) Vol. 70, Iss. 3
Closed Access | Times Cited: 6
Input-Output Selection for LSTM-Based Reduced-Order State Estimator Design
Sarupa Debnath, Soumya Ranjan Sahoo, Bernard T. Agyeman, et al.
Mathematics (2023) Vol. 11, Iss. 2, pp. 400-400
Open Access | Times Cited: 4
Machine learning methods for predicting the key metabolic parameters of Halomonas elongata DSM 2581 T
Guanxue Lai, Junxiong Yu, Jing Wang, et al.
Applied Microbiology and Biotechnology (2023) Vol. 107, Iss. 17, pp. 5351-5365
Closed Access | Times Cited: 2
Modeling and simulation of the enzymatic kinetics for the production of Galactooligosaccharides (GOS) using an Artificial Neural Network hybrid model
Juan D. Hoyos, M.A. Noriega, Carlos A.M. Riascos
Digital Chemical Engineering (2023) Vol. 9, pp. 100132-100132
Open Access | Times Cited: 2
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