Bibliography#
Ethan Coon, Daniil Svyatsky, Ahmad Jan, Eugene Kikinzon, Markus Berndt, Adam Atchley, Dylan Harp, Gianmarco Manzini, Eitan Shelef, Konstantin Lipnikov, Rao Garimella, Chonggang Xu, David Moulton, Satish Karra, Scott Painter, Elchin Jafarov, and Sergi Molins. Advanced terrestrial simulator. [Computer Software] https://doi.org/10.11578/dc.20190911.1, sep 2019. URL: https://doi.org/10.11578/dc.20190911.1, doi:10.11578/dc.20190911.1.
Thomas M. Cover and Joy A. Thomas. Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing). Wiley-Interscience, 2006. ISBN 0471241954.
Erol Cromwell, Pin Shuai, Peishi Jiang, Ethan T. Coon, Scott L. Painter, J. David Moulton, Youzuo Lin, and Xingyuan Chen. Estimating watershed subsurface permeability from stream discharge data using deep neural networks. Frontiers in Earth Science, 2021. URL: https://www.frontiersin.org/article/10.3389/feart.2021.613011, doi:10.3389/feart.2021.613011.
Yong HU, Xinghe YU, Shengli LI, Gongyang CHEN, Yanli ZHOU, and Zhaopu GAO. Improving the accuracy of geological model by using seismic forward and inverse techniques. Petroleum Exploration and Development, 41(2):208–216, 2014. URL: https://www.sciencedirect.com/science/article/pii/S1876380414600240, doi:https://doi.org/10.1016/S1876-3804(14)60024-0.
P. Jiang, P. Shuai, A. Sun, M. K. Mudunuru, and X. Chen. Knowledge-informed deep learning for hydrological model calibration: an application to coal creek watershed in colorado. Hydrology and Earth System Sciences, 27(14):2621–2644, 2023. URL: https://hess.copernicus.org/articles/27/2621/2023/, doi:10.5194/hess-27-2621-2023.
Vladimir M. Krasnopolsky and Helmut Schiller. Some neural network applications in environmental sciences. part i: forward and inverse problems in geophysical remote measurements. Neural Networks, 16(3):321–334, 2003. Neural Network Analysis of Complex Scientific Data: Astronomy and Geosciences. URL: https://www.sciencedirect.com/science/article/pii/S0893608003000273, doi:https://doi.org/10.1016/S0893-6080(03)00027-3.
Maruti K. Mudunuru, Kyongho Son, Peishi Jiang, Glenn Hammond, and Xingyuan Chen. Scalable deep learning for watershed model calibration. Frontiers in Earth Science, 2022. URL: https://www.frontiersin.org/journals/earth-science/articles/10.3389/feart.2022.1026479, doi:10.3389/feart.2022.1026479.
Peter Spirtes, Clark Glymour, and Richard Scheines. Causation, Prediction, and Search. The MIT Press, 01 2001. ISBN 9780262284158. URL: https://doi.org/10.7551/mitpress/1754.001.0001, doi:10.7551/mitpress/1754.001.0001.
A. Wang, P. Jiang, S. Burrows, S. Glienke, M. Ovchinnikov, and N. Mahfouz. Inverse mapping of the collision kernel and wall flux scaling in a large-scale convection-cloud chamber using local sensors and knowledge-informed deep learning. Manuscript to be submitted for Journal of Advances in Modeling Earth Systems, 2025.
Aaron Wang, Mikhail Ovchinnikov, Fan Yang, Silvio Schmalfuss, and Raymond A Shaw. Designing a convection-cloud chamber for collision-coalescence using large-eddy simulation with bin microphysics. Journal of Advances in Modeling Earth Systems, 16(1):e2023MS003734, 2024.
Jared Willard, Xiaowei Jia, Shaoming Xu, Michael Steinbach, and Vipin Kumar. Integrating scientific knowledge with machine learning for engineering and environmental systems. ACM Comput. Surv., November 2022. URL: https://doi.org/10.1145/3514228, doi:10.1145/3514228.