| Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems J Willard, X Jia, S Xu, M Steinbach, V Kumar ACM Computing Surveys, 2022 | 1071* | 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 | 334 | 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 | 312 | 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 | 288 | 2021 |
| 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 | 93 | 2021 |
| 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 | 75 | 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 | 62 | 2018 |
| Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality? C Varadharajan, AP Appling, B Arora, DS Christianson, VC Hendrix, ... Hydrological Processes 36 (4), e14565, 2022 | 50 | 2022 |
| Daily surface temperatures for 185,549 lakes in the conterminous United States estimated using deep learning (1980–2020) JD Willard, JS Read, S Topp, GJA Hansen, V Kumar Limnology and Oceanography Letters 7 (4), 287-301, 2022 | 27 | 2022 |
| Stream temperature predictions for river basin management in the Pacific Northwest and mid-Atlantic regions using machine learning H Weierbach, AR Lima, JD Willard, VC Hendrix, DS Christianson, ... Water 14 (7), 1032, 2022 | 22 | 2022 |
| Physics-guided recurrent graph networks for predicting flow and temperature in river networks X Jia, J Zwart, J Sadler, A Appling, S Oliver, S Markstrom, J Willard, S Xu, ... arXiv preprint arXiv:2009.12575, 2020 | 20 | 2020 |
| Integrating scientific knowledge with machine learning for engineering and environmental systems. arXiv 2020 J Willard, X Jia, S Xu, M Steinbach, V Kumar arXiv preprint arXiv:2003.04919, 2003 | 13 | 2003 |
| Invertibility aware integration of static and time-series data: An application to lake temperature modeling K Tayal, X Jia, R Ghosh, J Willard, J Read, V Kumar Proceedings of the 2022 SIAM international conference on data mining (SDM …, 2022 | 11 | 2022 |
| Time series predictions in unmonitored sites: A survey of machine learning techniques in water resources JD Willard, C Varadharajan, X Jia, V Kumar Environmental Data Science 4, e7, 2025 | 9 | 2025 |
| Analyzing and exploring training recipes for large-scale transformer-based weather prediction JD Willard, P Harrington, S Subramanian, A Mahesh, TA O'Brien, ... arXiv preprint arXiv:2404.19630, 2024 | 8 | 2024 |
| Mini-Batch Learning Strategies for modeling long term temporal dependencies: A study in environmental applications S Xu, A Khandelwal, X Li, X Jia, L Liu, J Willard, R Ghosh, K Cutler, ... Proceedings of the 2023 SIAM international conference on data mining (SDM …, 2023 | 7 | 2023 |
| Data release: Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning J Willard, J Read, A Appling, S Oliver U.S. Geological Survey data release, https://doi.org/10.5066/P9I00WFR, 2021 | 4 | 2021 |
| Huge ensembles part i: Design of ensemble weather forecasts using spherical fourier neural operators A Mahesh, W Collins, B Bonev, N Brenowitz, Y Cohen, J Elms, ... arXiv preprint arXiv:2408.03100, 2024 | 3 | 2024 |
| Using machine learning to develop a predictive understanding of the impacts of extreme water cycle perturbations on river water quality C Varadharajan, V Kumar, J Willard, J Zwart, J Sadler, H Weierbach, ... Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States …, 2021 | 3 | 2021 |
| Process guided deep learning for modeling physical systems: an application in lake temperature modeling X Jia, J Willard, A Karpatne, JS Read, JA Zwart, M Steinbach, V Kumar IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium …, 2020 | 3 | 2020 |
Vipin KumarUniversity of Minnesota確認したメール アドレス: umn.edu