Eco-KGML Project Publications

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

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

Neog, A., Daw, A., Khorasgani, S. F., & 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

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

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