| Deep hybrid modeling of chemical process: Application to hydraulic fracturing MSF Bangi, JSI Kwon Computers & Chemical Engineering 134, 106696, 2020 | 153 | 2020 |
| Development of local dynamic mode decomposition with control: Application to model predictive control of hydraulic fracturing A Narasingam, JSI Kwon Computers & Chemical Engineering 106, 501-511, 2017 | 130 | 2017 |
| Crystal shape and size control using a plug flow crystallization configuration JSI Kwon, M Nayhouse, G Orkoulas, PD Christofides Chemical Engineering Science 119, 30-39, 2014 | 124 | 2014 |
| Koopman Lyapunov‐based model predictive control of nonlinear chemical process systems A Narasingam, JSI Kwon AIChE Journal 65 (11), e16743, 2019 | 112 | 2019 |
| Machine learning-based adaptive model identification of systems: Application to a chemical process B Bhadriraju, A Narasingam, JSI Kwon Chemical Engineering Research and Design 152, 372-383, 2019 | 111 | 2019 |
| Modeling and control of crystal shape in continuous protein crystallization JSI Kwon, M Nayhouse, PD Christofides, G Orkoulas Chemical Engineering Science 107, 47-57, 2014 | 111 | 2014 |
| Temporal clustering for order reduction of nonlinear parabolic PDE systems with time‐dependent spatial domains: Application to a hydraulic fracturing process A Narasingam, P Siddhamshetty, J Sang‐Il Kwon AIChE Journal 63 (9), 3818-3831, 2017 | 103 | 2017 |
| Deep neural network-based hybrid modeling and experimental validation for an industry-scale fermentation process: Identification of time-varying dependencies among parameters P Shah, MZ Sheriff, MSF Bangi, C Kravaris, JSI Kwon, C Botre, J Hirota Chemical Engineering Journal 441, 135643, 2022 | 102 | 2022 |
| Development of a hybrid model for a partially known intracellular signaling pathway through correction term estimation and neural network modeling D Lee, A Jayaraman, JS Kwon PLoS Computational Biology 16 (12), e1008472, 2020 | 100 | 2020 |
| Data-driven identification of interpretable reduced-order models using sparse regression A Narasingam, JSI Kwon Computers & Chemical Engineering 119, 101-111, 2018 | 97 | 2018 |
| Operable adaptive sparse identification of systems: Application to chemical processes B Bhadriraju, MSF Bangi, A Narasingam, JSI Kwon AIChE Journal 66 (11), e16980, 2020 | 95 | 2020 |
| Modeling and control of protein crystal shape and size in batch crystallization JSI Kwon, M Nayhouse, PD Christofides, G Orkoulas AIChE Journal 59 (7), 2317-2327, 2013 | 91 | 2013 |
| Exploring the potential of time-series transformers for process modeling and control in chemical systems: an inevitable paradigm shift? N Sitapure, JSI Kwon Chemical Engineering Research and Design 194, 461-477, 2023 | 88 | 2023 |
| Physics-informed neural networks for hybrid modeling of lab-scale batch fermentation for β-carotene production using Saccharomyces cerevisiae MSF Bangi, K Kao, JSI Kwon Chemical Engineering Research and Design 179, 415-423, 2022 | 88 | 2022 |
| Multiscale modeling and operation of PECVD of thin film solar cells M Crose, JSI Kwon, M Nayhouse, D Ni, PD Christofides Chemical Engineering Science 136, 50-61, 2015 | 78 | 2015 |
| Modeling of hydraulic fracturing and designing of online pumping schedules to achieve uniform proppant concentration in conventional oil reservoirs P Siddhamshetty, S Yang, JSI Kwon Computers & Chemical Engineering 114, 306-317, 2018 | 75 | 2018 |
| Handling spatial heterogeneity in reservoir parameters using proper orthogonal decomposition based ensemble Kalman filter for model-based feedback control of hydraulic fracturing A Narasingam, P Siddhamshetty, JSI Kwon Industrial & Engineering Chemistry Research 57 (11), 3977-3989, 2018 | 75 | 2018 |
| Impact of proppant pumping schedule on well production for slickwater fracturing S Mao, P Siddhamshetty, Z Zhang, W Yu, T Chun, JSI Kwon, K Wu Spe Journal 26 (01), 342-358, 2021 | 74 | 2021 |
| Hybrid Koopman model predictive control of nonlinear systems using multiple EDMD models: An application to a batch pulp digester with feed fluctuation SH Son, HK Choi, J Moon, JSI Kwon Control Engineering Practice 118, 104956, 2022 | 70 | 2022 |
| Developing quantitative structure–property relationship models to predict the upper flammability limit using machine learning S Yuan, Z Jiao, N Quddus, JSII Kwon, CV Mashuga Industrial & Engineering Chemistry Research 58 (8), 3531-3537, 2019 | 70 | 2019 |
Panagiotis D. ChristofidesWilliam D. Van Vorst Distinguished Professor and Department Chair of Chemical Engineering, UCLAVerified email at seas.ucla.edu