Superior training of artificial neural networks using weight-space partitioning. - Hoshin V. Gupta; Kuo-lin Hsu; Soroosh Sorooshian (ICNN, 1997)

WaterNet the NASA water cycle solutions network. - Paul R. Houser; Deborah R. Belvedere; W. Pozzi; C. Schlosser; B. Imam; Hoshin V. Gupta; R. Schiffer; Claire Welty; R. Lawford; Charles Vorosmarty; Dave Matthews (IGARSS, 2007)

Linking science with environmental decision making: Experiences from an integrated modeling approach to supporting sustainable water resources management. - Yuqiong Liu; Hoshin V. Gupta; Everett Springer; Thorsten Wagener (Environ. Model. Softw., 2008)

A formal framework for scenario development in support of environmental decision-making. - Mohammed Mahmoud; Yuqiong Liu; Holly Hartmann; Steven Stewart; Thorsten Wagener; Darius J. Semmens; Robert N. Stewart; Hoshin V. Gupta; Damian Dominguez; Francina Dominguez; David W. Hulse; Rebecca A. Letcher; et al. (Environ. Model. Softw., 2009)

Scenario development for water resources planning and watershed management: Methodology and semi-arid region case study. - Mohammed Mahmoud; Hoshin V. Gupta; Seshadri Rajagopal (Environ. Model. Softw., 2011)

A multi-method Generalized Global Sensitivity Matrix approach to accounting for the dynamical nature of earth and environmental systems models. - Saman Razavi; Hoshin V. Gupta (Environ. Model. Softw., 2019)

VARS-TOOL: A toolbox for comprehensive, efficient, and robust sensitivity and uncertainty analysis. - Saman Razavi; Razi Sheikholeslami; Hoshin V. Gupta; Amin Haghnegahdar (Environ. Model. Softw., 2019)

Global sensitivity analysis for high-dimensional problems: How to objectively group factors and measure robustness and convergence while reducing computational cost. - Razi Sheikholeslami; Saman Razavi; Hoshin V. Gupta; William Becker; Amin Haghnegahdar (Environ. Model. Softw., 2019)

Nowcasting-Nets: Deep Neural Network Structures for Precipitation Nowcasting Using IMERG. - Mohammad Reza Ehsani; Ariyan Zarei; Hoshin V. Gupta; Kobus Barnard; Ali Behrangi (CoRR, 2021)

Computing Accurate Probabilistic Estimates of One-D Entropy from Equiprobable Random Samples. - Hoshin V. Gupta; Mohammed Reza Ehsani; Tirthankar Roy; Maria A. Sans-Fuentes; Uwe Ehret; Ali Behrangi (Entropy, 2021)

The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support. - Saman Razavi; Anthony J. Jakeman; Andrea Saltelli; Clémentine Prieur; Bertrand Iooss; Emanuele Borgonovo; Elmar Plischke; Samuele Lo Piano; Takuya Iwanaga; William E. Becker; Stefano Tarantola; Joseph H. A. Guillaume; et al. (Environ. Model. Softw., 2021)

How certain are our uncertainty bounds? Accounting for sample variability in Monte Carlo-based uncertainty estimates. - Tirthankar Roy; Hoshin V. Gupta (Environ. Model. Softw., 2021)

NowCasting-Nets: Representation Learning to Mitigate Latency Gap of Satellite Precipitation Products Using Convolutional and Recurrent Neural Networks. - Mohammad Reza Ehsani; Ariyan Zarei; Hoshin Vijai Gupta; Kobus Barnard; Eric Lyons 002; Ali Behrangi (IEEE Trans. Geosci. Remote. Sens., 2022)

Differentiable modeling to unify machine learning and physical models and advance Geosciences. - Chaopeng Shen; Alison P. Appling; Pierre Gentine; Toshiyuki Bandai; Hoshin V. Gupta; Alexandre Tartakovsky; Marco Baity-Jesi; Fabrizio Fenicia; Daniel Kifer; Li Li; Xiaofeng Liu; Wei Ren; et al. (CoRR, 2023)

A Mass-Conserving-Perceptron for Machine Learning-Based Modeling of Geoscientific Systems. - Yuan-Heng Wang; Hoshin V. Gupta (CoRR, 2023)

On how data are partitioned in model development and evaluation: Confronting the elephant in the room to enhance model generalization. - Holger R. Maier; Feifei Zheng; Hoshin V. Gupta; Junyi Chen; Juliane Mai; Dragan A. Savic; Ralf Loritz; Wenyan Wu 001; Danlu Guo; Andrew Bennett; Anthony J. Jakeman; Saman Razavi; et al. (Environ. Model. Softw., 2023)

Towards Interpretable Physical-Conceptual Catchment-Scale Hydrological Modeling using the Mass-Conserving-Perceptron. - Yuan-Heng Wang; Hoshin V. Gupta (CoRR, 2024)

Using Machine Learning to Discover Parsimonious and Physically-Interpretable Representations of Catchment-Scale Rainfall-Runoff Dynamics. - Yuan-Heng Wang; Hoshin V. Gupta (CoRR, 2024)

On the Accurate Estimation of Information-Theoretic Quantities from Multi-Dimensional Sample Data. - Manuel Álvarez Chaves; Hoshin V. Gupta; Uwe Ehret; Anneli Guthke (Entropy, 2024)

An R package to partition observation data used for model development and evaluation to achieve model generalizability. - Yiran Ji; Feifei Zheng; Jinhua Wen; Qifeng Li; Junyi Chen; Holger R. Maier; Hoshin V. Gupta (Environ. Model. Softw., 2025)