A physics-informed deep learning approach for bearing fault detection. - Sheng Shen 006; Hao Lu 020; Mohammadkazem Sadoughi; Chao Hu; Venkat Nemani; Adam Thelen; Keith Webster; Matthew J. Darr; Jeff Sidon; Shawn Kenny (Eng. Appl. Artif. Intell., 2021)

A Comprehensive Review of Digital Twin - Part 2: Roles of Uncertainty Quantification and Optimization, a Battery Digital Twin, and Perspectives. - Adam Thelen; Xiaoge Zhang 001; Olga Fink; Yan Lu; Sayan Ghosh; Byeng D. Youn; Michael D. Todd; Sankaran Mahadevan; Chao Hu; Zhen Hu (CoRR, 2022)

A Comprehensive Review of Digital Twin - Part 1: Modeling and Twinning Enabling Technologies. - Adam Thelen; Xiaoge Zhang 001; Olga Fink; Yan Lu; Sayan Ghosh; Byeng D. Youn; Michael D. Todd; Sankaran Mahadevan; Chao Hu; Zhen Hu (CoRR, 2022)

Ensembles of probabilistic LSTM predictors and correctors for bearing prognostics using industrial standards. - Venkat Nemani; Hao Lu; Adam Thelen; Chao Hu; Andrew T. Zimmerman (Neurocomputing, 2022)

Federated Learning with Uncertainty-Based Client Clustering for Fleet-Wide Fault Diagnosis. - Hao Lu; Adam Thelen; Olga Fink; Chao Hu; Simon Laflamme (CoRR, 2023)

Predicting Battery Lifetime Under Varying Usage Conditions from Early Aging Data. - Tingkai Li; Zihao Zhou; Adam Thelen; David A. Howey; Chao Hu (CoRR, 2023)

Physics-Informed Machine Learning for Battery Degradation Diagnostics: A Comparison of State-of-the-Art Methods. - Sina Navidi; Adam Thelen; Tingkai Li; Chao Hu (CoRR, 2024)