| A comprehensive review of digital twin—part 1: modeling and twinning enabling technologies A Thelen, X Zhang, O Fink, Y Lu, S Ghosh, BD Youn, MD Todd, ... Structural and Multidisciplinary Optimization 65 (12), 354, 2022 | 131 | 2022 |
| A physics-informed deep learning approach for bearing fault detection S Shen, H Lu, M Sadoughi, C Hu, V Nemani, A Thelen, K Webster, M Darr, ... Engineering Applications of Artificial Intelligence 103, 104295, 2021 | 129 | 2021 |
| A comprehensive review of digital twin—part 2: roles of uncertainty quantification and optimization, a battery digital twin, and perspectives A Thelen, X Zhang, O Fink, Y Lu, S Ghosh, BD Youn, MD Todd, ... Structural and multidisciplinary optimization 66 (1), 1, 2023 | 54 | 2023 |
| Integrating physics-based modeling and machine learning for degradation diagnostics of lithium-ion batteries A Thelen, YH Lui, S Shen, S Laflamme, S Hu, H Ye, C Hu Energy Storage Materials 50, 668-695, 2022 | 49 | 2022 |
| Augmented model-based framework for battery remaining useful life prediction A Thelen, M Li, C Hu, E Bekyarova, S Kalinin, M Sanghadasa Applied Energy 324, 119624, 2022 | 35 | 2022 |
| Ensembles of probabilistic LSTM predictors and correctors for bearing prognostics using industrial standards VP Nemani, H Lu, A Thelen, C Hu, AT Zimmerman Neurocomputing 491, 575-596, 2022 | 24 | 2022 |
| Multi-step ahead state estimation with hybrid algorithm for high-rate dynamic systems M Nelson, V Barzegar, S Laflamme, C Hu, ARJ Downey, JD Bakos, ... Mechanical Systems and Signal Processing 182, 109536, 2023 | 7 | 2023 |
| Physics-informed machine learning for degradation diagnostics of lithium-ion batteries A Thelen, YH Lui, S Shen, S Laflamme, S Hu, C Hu International design engineering technical conferences and computers and …, 2021 | 6 | 2021 |
| Federated learning with uncertainty-based client clustering for fleet-wide fault diagnosis H Lu, A Thelen, O Fink, C Hu, S Laflamme Mechanical Systems and Signal Processing 210, 111068, 2024 | 3 | 2024 |
| Degradation-aware ensemble of diverse predictors for remaining useful life prediction V Nemani, A Thelen, C Hu, S Daining Journal of Mechanical Design 145 (3), 031706, 2023 | 3 | 2023 |
| Health index construction with feature fusion optimization for predictive maintenance of physical systems V Nemani, A Bray, A Thelen, C Hu, S Daining Structural and Multidisciplinary Optimization 65 (12), 349, 2022 | 3 | 2022 |
| An end-to-end learning framework for early prediction of battery capacity trajectory J Liu, A Thelen, C Hu, XG Yang Annual Conference of the PHM Society 13 (1), 2021 | 3 | 2021 |
| Physics-Informed Neural Networks for Degradation Diagnostics of Lithium-Ion Batteries S Navidi, A Thelen, T Li, C Hu International Design Engineering Technical Conferences and Computers and …, 2023 | 2 | 2023 |
| Dynamically weighted ensemble of diverse learners for remaining useful life prediction V Nemani, A Thelen, C Hu, S Daining International Design Engineering Technical Conferences and Computers and …, 2022 | 2 | 2022 |
| Predicting battery lifetime under varying usage conditions from early aging data T Li, Z Zhou, A Thelen, DA Howey, C Hu Cell Reports Physical Science 5 (4), 2024 | 1 | 2024 |
| Physics-informed machine learning for battery degradation diagnostics: A comparison of state-of-the-art methods S Navidi, A Thelen, T Li, C Hu Energy Storage Materials 68, 103343, 2024 | 1 | 2024 |
| Sequential Bayesian optimization for accelerating the design of sodium metal battery nucleation layers A Thelen, M Zohair, J Ramamurthy, A Harkaway, W Jiao, M Ojha, ... Journal of Power Sources 581, 233508, 2023 | 1 | 2023 |
| Probabilistic machine learning for battery health diagnostics and prognostics—review and perspectives A Thelen, X Huan, N Paulson, S Onori, Z Hu, C Hu npj Materials Sustainability 2 (1), 14, 2024 | | 2024 |
| Degradation Diagnostics of Lithium-Ion Batteries Cycled Under Varying Conditions T Li, A Thelen, N Karan, J Liu, C Tischer, C Hu Electrochemical Society Meeting Abstracts 243, 850-850, 2023 | | 2023 |
| A Comparative Study on Physics-Informed Machine Learning for Battery Degradation Diagnostics S Navidi, A Thelen, T Li, C Hu Electrochemical Society Meeting Abstracts 243, 848-848, 2023 | | 2023 |
Chao HuAssociate Professor at University of Connecticut在 uconn.edu 的电子邮件经过验证
