Eco-KGML Project Publications

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Publications

• Yu, R., Ladwig, R., Xu, X., Zhu, P., Hanson, P. C., Xie, Y., & Jia, X. (2024). Evolution-Based Feature Selection for Predicting Dissolved Oxygen Concentrations in Lakes. In M. Affenzeller, S. M. Winkler, A. V. Kononova, H. Trautmann, T. Tušar, P. Machado, & T. Bäck (Eds.), Parallel Problem Solving from Nature – PPSN XVIII (pp. 398–415). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-70085-9_25
• Ladwig, R., Daw, A., Albright, E. A., Buelo, C., Karpatne, A., Meyer, M. F., Neog, A., Hanson, P. C., & Dugan, H. A. (2024). Modular Compositional Learning Improves 1D Hydrodynamic Lake Model Performance by Merging Process-Based Modeling With Deep Learning. Journal of Advances in Modeling Earth Systems, 16(1), e2023MS003953. https://doi.org/10.1029/2023MS003953
• Hanson, P. C., Ladwig, R., Buelo, C., Albright, E. A., Delany, A. D., & Carey, C. C. (2023). Legacy Phosphorus and Ecosystem Memory Control Future Water Quality in a Eutrophic Lake. Journal of Geophysical Research: Biogeosciences, 128(12), e2023JG007620. https://doi.org/10.1029/2023JG007620
• Daw, A., Bu, J., Wang, S., Perdikaris, P., & Karpatne, A. (2023). Mitigating Propagation Failures in Physics-informed Neural Networks using Retain-Resample-Release (R3) Sampling. Proceedings of the 40th International Conference on Machine Learning, 7264–7302. https://proceedings.mlr.press/v202/daw23a.html

Manuscripts Accepted for Publication

• McAfee, B. J., Pradhan, A., Neog, A., Fatemi, S., Hensley, R. T., Lofton, M. E., Karpatne, A., Carey, C. C., & Hanson, P. C. (2025). LakeBeD-US: A benchmark dataset for lake water quality time series and vertical profiles. Earth System Science Data Discussions. https://doi.org/10.5194/essd-2025-27

Peer-Reviewed Non-Archival Conference Papers

• Neog, A., Sawhney, M., Mehrab, K. S., Fatemi, S., Lofton, M. E., Dutta, A., Pradhan, A., McAfee, B. J., Marchisin, E., Ladwig, R., Daw, A., Carey, C. C., Hanson, P., & Karpatne, A. (2025, June 9). Toward Scientific Foundation Models for Aquatic Ecosystems. 1st ICML Workshop on Foundation Models for Structured Data. https://openreview.net/forum?id=hxMPNdhfIO

Preprints

• Lofton, M. E., Thomas, R. Q., Olsson, F., Breef-Pilz, A., Carey, C. C., Neog, A., Fatemi, S., Karpatne, A., Howard, D., & Wander, H. (2025). The importance of a multi-model ensemble for predicting variable ecological time series across dynamic conditions. ESS Open Archive. https://doi.org/10.22541/essoar.174785900.05100209/v1
• Neog, A., Daw, A., Fatemi Khorasgani, S., & Karpatne, A. (2025). Masking the Gaps: An Imputation-Free Approach to Time Series Modeling with Missing Data (arXiv:2502.15785). arXiv. https://doi.org/10.48550/arXiv.2502.15785
• Yu, R., Qiu, C., Ladwig, R., Hanson, P., Xie, Y., & Jia, X. (2025). Physics-Guided Foundation Model for Scientific Discovery: An Application to Aquatic Science (arXiv:2502.06084). arXiv. https://doi.org/10.48550/arXiv.2502.06084
• Yu, R., Qiu, C., Ladwig, R., Hanson, P. C., Xie, Y., Li, Y., & Jia, X. (2024). Adaptive Process-Guided Learning: An Application in Predicting Lake DO Concentrations (arXiv:2411.12973). arXiv. https://doi.org/10.48550/arXiv.2411.12973
• Hounshell, A. G., Lewis, A. S. L., Howard, D. W., Wander, H. L., Lofton, M. E., Hanson, P. C., & Carey, C. C. (2024). Dissolved organic carbon dynamics are driven by water temperature, primary production, and anoxia over five years of whole-ecosystem experiments in a eutrophic reservoir. ESS Open Archive. https://doi.org/10.22541/essoar.172926851.15664834/v1
• Karpatne, A., Jia, X., & Kumar, V. (2024). Knowledge-guided Machine Learning: Current Trends and Future Prospects (arXiv:2403.15989). arXiv. https://doi.org/10.48550/arXiv.2403.15989

Graduate Student Theses

• Pradhan, A. K. (2025). Training Physics-Guided Neural Networks with Multiple Constraints: An Application in Lake Ecology Modeling [Master’s Thesis, Virginia Tech]. VTechWorks. https://hdl.handle.net/10919/134207
• McAfee, B. J. (2024). Advancing Data and Tools for Machine Learning Modeling of Lake Water Quality [Master’s Thesis, University of Wisconsin–Madison]. MINDS@UW. http://digital.library.wisc.edu/1793/89695

Reports

• Kumar, V., Zhang, A., Karpatne, A., Rashidi, P., Wang, H., Abolhasani, M., Apte, C., Banerjee, A., Boyda, D., Calabrese, M., Chawla, N., Chellappa, R., Forsyth, D., Foster, I., Gil, Y., Gomes, C., Han, J., Hanson, P., Karypis, G., … Wang, W. (2023). Report on 2023 NSF Sponsored Workshop on AI-Enabled Scientific Revolution. University Digital Conservancy. https://hdl.handle.net/11299/270005