| A transfer learning approach for predictive modeling of bioprocesses using small data AW Rogers, F Vega‐Ramon, J Yan, EA del Río‐Chanona, K Jing, ... Biotechnology and Bioengineering 119 (2), 411-422, 2022 | 34 | 2022 |
| Investigating ‘greyness’ of hybrid model for bioprocess predictive modelling AW Rogers, Z Song, FV Ramon, K Jing, D Zhang Biochemical Engineering Journal 190, 108761, 2023 | 17 | 2023 |
| A reinforcement learning‐based hybrid modeling framework for bioprocess kinetics identification MR Mowbray, C Wu, AW Rogers, EAD Rio‐Chanona, D Zhang Biotechnology and Bioengineering 120 (1), 154-168, 2023 | 14 | 2023 |
| Investigating physics-informed neural networks for bioprocess hybrid model construction AW Rogers, IOS Cardenas, EA Del Rio-Chanona, D Zhang Computer Aided Chemical Engineering 52, 83-88, 2023 | 4 | 2023 |
| Kinetic modelling of γ-linolenic acid production by Cunninghamella echinulata Z Song, A Rogers, BA Cho, K Jing, D Zhang Computer Aided Chemical Engineering 51, 109-114, 2022 | 3 | 2022 |
| Integrating knowledge-guided symbolic regression and model-based design of experiments to automate process flow diagram development AW Rogers, A Lane, C Mendoza, S Watson, A Kowalski, P Martin, ... arXiv preprint arXiv:2405.04592, 2024 | | 2024 |
| Hybrid Model Construction AW Rogers, D Zhang | | 2023 |
| Hybrid Modelling Under Uncertainty: Effects of Model Greyness, Data Quality and Data Quantity AW Rogers, Z Song, F Vega Ramon, K Jing, D Zhang | | 2023 |
| Integrating hybrid modelling and transfer learning for new bioprocess predictive modelling S Kay, H Kay, AW Rogers, D Zhang Computer Aided Chemical Engineering 52, 2595-2600, 2023 | | 2023 |
