2021

J. Willard, J. Read, A. Appling, S. Oliver, X. Jia, and V. Kumar. Predicting Water Temperature Dynamics of Unmonitored Lakes With Meta‐Transfer Learning, Water Resources Research, 57, 2021. https://doi.org/10.1029/2021WR029579

J. Willard, X. Jia, S. Xu, M. Steinbach, and V. Kumar. Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems. 2021. https://arxiv.org/abs/2003.04919

K.J. Mayer, E.A. Barnes. Subseasonal Forecasts of Opportunity Identified by an Explainable Neural Network, Geophysical Research Letters, May 4, 2021, https://doi.org/10.1029/2020gl092092.

X. Jia, J. Zwart, J. Sadler, A. Appling, S. Oliver, S. Markstrom, J. Willard, S. Xu, M. Steinbach, V. Kumar. Physics-Guided Recurrent Graph Networks for Predicting Flow and Temperature in River Networks. SIAM International Conference on Data Mining, April, 2021.

T. Beucler, I. Ebert-Uphoff, S. Rasp, M. Pritchard, P. Gentine, Machine Learning for Clouds and Climate, book chapter in Geophysical Monograph Series, accepted Mar 2021, Preprint here.

Samarasinghe, S.M., Barnes, E.A., Connolly, C., Ebert-Uphoff, I., Sun, L. Strengthened causal connections between the MJO and the North Atlantic with climate warming, Geophysical Research Letters, Feb 1 2021, https://doi.org/10.1029/2020GL091168. Featured as research highlight in Nature Climate Change: B. Langenbrunner, The Madden–Julian oscillation strengthens its reach. Nature Climate Change, 11, 183, 3 March 2021. https://doi.org/10.1038/s41558-021-01008-7.

Y. Lee, C.D. Kummerow, and I. Ebert-Uphoff. Applying machine learning methods to detect convection using GOES-16 ABI data, Atmospheric Measurement Techniques, accepted March 2021, https://doi.org/10.5194/amt-2020-420.

X. Jia, J. Willard, A. Karpatne, J. Read, J. Zwart, M. Steinbach, V. Kumar. Physics-Guided Machine Learning for Scientific Discovery: An Application in Simulating Lake Temperature Profiles. ACM Transactions on Data Science (Accepted for publication) 2021 https://arxiv.org/pdf/2001.11086.pdf

2020

P.C. Hanson, A.B. Stillman, X. Jia, A. Karpatne, H. Dugan, C. C. Carey, J. Stachelek, N. Ward, Y. Zhang, J. Read, and V. Kumar, (2020). Predicting lake surface water phosphorus dynamics using process-guided machine learning. Ecological Modelling, 430, 109136. https://doi.org/10.1016/j.ecolmodel.2020.109136

X. Li, J. Nieber, C. Duffy, A. Khandelwal, S. Xu, V. Kumar and M. Steinbach, (2020). KGML Implementation for Predicting Watershed Discharge: Case Study for the South Branch of the Root River Watershed, Minnesota Water Resources Conference, October 16, 2020. Abstract

Barnes, E. A., Toms, B., Hurrell, J. W., Ebert-Uphoff, I., Anderson, C., & Anderson, D. (2020). Indicator patterns of forced change learned by an artificial neural network, submitted to Journal of Advances in Modeling Earth Systems (JAMES), August 2020. https://doi.org/10.1029/2020MS002195

Ebert-Uphoff, I and Hilburn, K. A., (2020). Evaluation, Tuning and Interpretation of Neural Networks for Working with Images in Meteorological Applications. Submitted to Bulletin of the American Meteorological Society, August 2020. https://doi.org/10.1175/BAMS-D-20-0097.1

Jared Willard, Xiaowei Jia, Shaoming Xu, Michael Steinbach, Vipin Kumar. Integrating Physics-Based Modeling with Machine Learning: A Survey. April 2020. https://arxiv.org/abs/2003.04919

Xiaowei Jia, Jared Willard, Anuj Karpatne, Jordan Read, Jacob Zwart, Michael Steinbach, Vipin Kumar. Physics-Guided Machine Learning for Scientific Discovery: An Application in Simulating Lake Temperature Profiles. January 2020. https://arxiv.org/pdf/2001.11086.pdf

Hilburn, K. A., Ebert-Uphoff, I., and Miller, S. D., (2020) Development and Interpretation of a Neural Network-Based Synthetic Radar Reflectivity Estimator Using GOES-R Satellite Observations, Journal of Applied Meteorology and Climatology (in press). Preprint: https://arxiv.org/abs/2004.07906

Toms, B. A., Barnes, E. A., & Ebert‐Uphoff, I. (2020). Physically interpretable neural networks for the geosciences: Applications to earth system variability. Journal of Advances in Modeling Earth Systems, 12(9). https://doi.org/10.1029/2019MS002002

Samarasinghe, S. M., Deng, Y., & Ebert-Uphoff, I. (2020). A Causality-Based View of the Interaction between Synoptic-and Planetary-Scale Atmospheric Disturbances. Journal of the Atmospheric Sciences, 77(3), 925-941. https://doi.org/10.1175/JAS-D-18-0163.1

2019

Barnes, E. A., Hurrell, J. W., Ebert‐Uphoff, I., Anderson, C., & Anderson, D., Viewing forced climate patterns through an AI Lens. Geophysical Research Letters, 46(22), 13389-13398, https://doi.org/10.1029/2019GL084944, Nov 2019.

Xiaowei Jia, Jared Willard, Anuj Karpatne, Jordan Stuart Read, Jacob A Zwart, Alison Appling, Paul C Hanson, Vipin Kumar. Physics Guided Machine Learning: A New Paradigm for Modeling Dynamic Systems . AGU Fall Meeting, December 2019.

Xiaowei Jia, Jared Willard, Anuj Karpatne, Jordan Read, Jacob Zwart, Michael Steinbach, Vipin Kumar. Physics Guided RNNs for Modeling Dynamical Systems: A Case Study in Simulating Lake Temperature Profiles. Proceedings of the 2019 SIAM International Conference on Data Mining, May 2019. doi: 101137/1.9781611975673.63

Jordan S. Read, Xiaowei Jia, Jared Willard, Alison P. Appling, Jacob A. Zwart, Samantha K. Oliver, Anuj Karpatne, Gretchen J.A. Hansen, Paul C. Hanson, William Watkins, Michael Steinbach, Vipin Kumar. Process-Guided Deep Learning Predictions of Lake Water Temperature. 2019. Water Resources Research (55). https://doi.org/10.1029/2019WR024922

C. Duffy, G. l. Bhatt, L. Shu and A. Kemanian, 2019, Increasing the Value of Mechanistic Watershed Models Through Automation, Emulation and Machine Learning, CERF 2019 25th Biennial Conference, 3-7 November 2019, Mobile, AL (Coastal and Estuarine Research Foundation)

Tianle Ma and Aidong Zhang. Integrate Multi-omics Data with Biological Interaction Networks Using Multi-view Factorization AutoEncoder (MAE), BMC Genomics, December 2019.

2018

Anuj Karpatne, William Watkins, Jordan Read, Vipin Kumar. Physics-guided Neural Networks (PGNN): An Application in Lake Temperature Modeling. November 2018. https://arxiv.org/pdf/1710.11431.pdf

2017

Anuj Karpatne, Gowtham Atluri, James H. Faghmous, Michael Steinbach, Arindam Banerjee, Auroop Ganguly, Shashi Shekhar, Nagiza Samatova, Vipin Kumar. Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data. IEEE Transactions on Knowledge and Data Engineering, vol. 29, no. 10, pp. 2318-2331, 1 October 2017. https://ieeexplore.ieee.org/document/7959606