| Integrating physics-based modeling with machine learning: A survey J Willard, X Jia, S Xu, M Steinbach, V Kumar arXiv preprint arXiv:2003.04919 1 (1), 1-34, 2020 | 529 | 2020 |
| Integrating scientific knowledge with machine learning for engineering and environmental systems J Willard, X Jia, S Xu, M Steinbach, V Kumar ACM Computing Surveys 55 (4), 1-37, 2022 | 458 | 2022 |
| Process‐guided deep learning predictions of lake water temperature JS Read, X Jia, J Willard, AP Appling, JA Zwart, SK Oliver, A Karpatne, ... Water Resources Research 55 (11), 9173-9190, 2019 | 315 | 2019 |
| Physics Guided RNNs for Modeling Dynamical Systems: A Case Study in Simulating Lake Temperature Profiles X Jia, J Willard, A Karpatne, J Read, J Zwart, M Steinbach, V Kumar Proceedings of the 2019 SIAM International Conference on Data Mining, 558-566, 2019 | 299 | 2019 |
| Physics-guided machine learning for scientific discovery: An application in simulating lake temperature profiles X Jia, J Willard, A Karpatne, JS Read, JA Zwart, M Steinbach, V Kumar ACM/IMS Transactions on Data Science 2 (3), 1-26, 2021 | 272 | 2021 |
| A novel semi-supervised deep learning framework for affective state recognition on eeg signals X Jia, K Li, X Li, A Zhang 2014 IEEE international conference on bioinformatics and bioengineering, 30-37, 2014 | 106 | 2014 |
| Physics-Guided Recurrent Graph Model for Predicting Flow and Temperature in River Networks X Jia, J Zwart, J Sadler, A Appling, S Oliver, S Markstrom, J Willard, S Xu, ... Proceedings of the 2021 SIAM International Conference on Data Mining (SDM …, 2021 | 101* | 2021 |
| Predicting lake surface water phosphorus dynamics using process-guided machine learning PC Hanson, AB Stillman, X Jia, A Karpatne, HA Dugan, CC Carey, ... Ecological Modelling 430, 109136, 2020 | 87 | 2020 |
| Incremental Dual-memory LSTM in Land Cover Prediction X Jia, A Khandelwal, G Nayak, J Gerber, K Carlson, P West, V Kumar Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge …, 2017 | 87 | 2017 |
| Predicting Water Temperature Dynamics of Unmonitored Lakes With Meta‐Transfer Learning JD Willard, JS Read, AP Appling, SK Oliver, X Jia, V Kumar Water Resources Research 57 (7), e2021WR029579, 2021 | 73 | 2021 |
| Physics Guided Recurrent Neural Networks For Modeling Dynamical Systems: Application to Monitoring Water Temperature And Quality In Lakes X Jia, A Karpatne, J Willard, M Steinbach, J Read, PC Hanson, HA Dugan, ... arXiv preprint arXiv:1810.02880, 2018 | 60 | 2018 |
| Physics Guided Machine Learning Methods for Hydrology A Khandelwal, S Xu, X Li, X Jia, M Stienbach, C Duffy, J Nieber, V Kumar arXiv preprint arXiv:2012.02854, 2020 | 59 | 2020 |
| A statistically-guided deep network transformation and moderation framework for data with spatial heterogeneity Y Xie, E He, X Jia, H Bao, X Zhou, R Ghosh, P Ravirathinam 2021 IEEE International Conference on Data Mining (ICDM), 767-776, 2021 | 46 | 2021 |
| Detecting epileptic seizures with electroencephalogram via a context-learning model G Xun, X Jia, A Zhang BMC medical informatics and decision making 16, 97-109, 2016 | 45 | 2016 |
| KGML-ag: a modeling framework of knowledge-guided machine learning to simulate agroecosystems: a case study of estimating N2O emission using data from … L Liu, S Xu, J Tang, K Guan, TJ Griffis, MD Erickson, AL Frie, X Jia, T Kim, ... Geoscientific Model Development 15 (7), 2839-2858, 2022 | 40 | 2022 |
| Multi‐Task Deep Learning of Daily Streamflow and Water Temperature JM Sadler, AP Appling, JS Read, SK Oliver, X Jia, JA Zwart, V Kumar Water Resources Research 58 (4), e2021WR030138, 2022 | 38 | 2022 |
| A comprehensive investigation of LSTM-CNN deep learning model for fast detection of combustion instability Z Lyu, X Jia, Y Yang, K Hu, F Zhang, G Wang Fuel 303, 121300, 2021 | 37 | 2021 |
| Heterogeneous Spatio-Temporal Graph Convolution Network for Traffic Forecasting with Missing Values W Zhong, Q Suo, X Jia, A Zhang, L Su 2021 IEEE 41st International Conference on Distributed Computing Systems …, 2021 | 34 | 2021 |
| Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems L Liu, W Zhou, K Guan, B Peng, S Xu, J Tang, Q Zhu, J Till, X Jia, C Jiang, ... Nature Communications 15 (1), 357, 2024 | 31 | 2024 |
| Bringing automated, remote‐sensed, machine learning methods to monitoring crop landscapes at scale X Jia, A Khandelwal, DJ Mulla, PG Pardey, V Kumar Agricultural Economics 50, 41-50, 2019 | 31 | 2019 |
Vipin KumarUniversity of MinnesotaVerified email at umn.edu
