A. Markowitz, R. Abuaamoud, S. Ben Ayed, A. Rupe, R. Yang, T. Kliphuis, V.V. Vesselinov, AI-augmented geothermal model for scalable energy uncertainties in buildings, Scientific Reports, 10.1038/s41598-026-40837-4, 2026 Details Link P. Lara, N.R. Ranasinghe, V.V. Vesselinov, C.W. Johnson, Deep-Embedded-Clustering of Microseismicity Identifies Multiple Failure Mechanisms at The Geysers Geothermal Field, Geophysics (submitted), 10.22541/essoar.177265440.06972526/v1, 2026 Details Link E.E. Jafarov, H. Genet, V.V. Vesselinov, V. Briones, A. Kabeer, A.L. Mullen, B. Maglio, T. Carman, R. Rutter, J. Clein, C.-C. Chang, D. Teber, T. Smith, J.M. Rady, C. Schädel, J.D. Watts, B.M. Rogers, S.M. Natali, Estimation of above- and below-ground ecosystem parameters for DVM-DOS-TEM v0.7.0 using MADS v1.7.3, Geoscientific Model Development, 10.5194/gmd-18-3857-2025, 2025 Details Link T.L. Kliphuis, A. Markowitz, R. Yang, V.V. Vesselinov, GeoDAWN To GeoTGo: From Complex Data To Decisions Related To Geothermal Prospectivity, Stanford Geothermal Workshop, Stanford, CA, 2025 Details Link V.V. Vesselinov, H. Jasperson, T.L. Kliphuis, Machine-Learning Methods and Tools Designed for Community-Based Equitable and Inclusive Geothermal Development, Stanford Geothermal Workshop, Stanford, CA, 2024 Details PDF PDF missing locally (link kept as-is)
V.V. Vesselinov, T.L. Kliphuis, ChemML: Physics-Informed AI/ML of Geochemical Datasets for Characterization, Parameterization, and Prediction of Contaminant Transport and Remediation Processes, DOE, 2023 Details Link D. O'Malley, S.Y. Greer, A. Pachalieva, W. Hao, D. Harp, V.V. Vesselinov, DPFEHM: a differentiable subsurface physics simulator, Journal of Open Source Software, 10.21105/joss.04560, 2023 Details Link M. Mudunuru, B. Ahmmed, E. Rau, V.V. Vesselinov, S. Karra, Machine Learning for Geothermal Resource Exploration in Tularosa Basin, New Mexico, Energies, 10.3390/en16073098, 2023 Details PDF K.C. Solander, C.J. Talsma, V.V. Vesselinov, The drivers and predictability of wildfire re-burns in the western United States (US), Environ. Res.: Climate 2, 10.1088/2752-5295/acb079, 2023 Details PDF B. Ahmmed, M.K. Mudunuru, L.P. Frash, V.V. Vesselinov, A Progress Report on GeoThermalCloud: Framework An Open-Source Machine Learning Based Tool for Discovery, Exploration, and Development of Hidden Geothermal Resources, Stanford Geothermal Workshop, Stanford, CA, 2022 Details PDF PDF missing locally (link kept as-is)
J. Bennett, J. Ogland-Hand, K. Cox, P. Johnson, E. Middleton, A. Pompilio, S. Samal, C. Talsma, V.V. Vesselinov, K. Ellett, R. Middleton, Beam Me Up SCO2TPRO: A Comparison to the FE/NETL CO2 Saline Storage Cost Model and Updates on Tool Development, SSRN Electronic Journal, 10.2139/ssrn.4275200, 2022 Details Link C.J. Talsma, K.E. Bennett, V.V. Vesselinov, Characterizing Drought Behavior in the Colorado River Basin Using Unsupervised Machine Learning, Earth and Space Science, 10.1029/2021ea002086, 2022 Details Link V.V. Vesselinov, B. Ahmmed, M. Mudunuru, S. Karra, R. Middleton, Discovering hidden geothermal signatures using non-negative matrix factorization with customized k-means clustering, Geothermics, 10.1016/j.geothermics.2022.102576, 2022 Details PDF V.V. Vesselinov, B. Ahmmed, M. Mudunuru, S. Karra, R. Middleton, GeoThermalCloud: Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources, Stanford Geothermal Workshop, Stanford, CA, 2022 Details PDF PDF missing locally (link kept as-is)
M. Mudunuru, V.V. Vesselinov, B. Ahmmed, GeoThermalCloud: Machine learning for geothermal resource exploration, Journal of Machine Learning for Modeling and Computing, 10.1615/JMachLearnModelComput.2022046445, 2022 Details PDF H. Wu, D. O'Malley, J.K. Golden, V.V. Vesselinov, Inverse Analysis with Variational Autoencoders: A Comparison of Shallow and Deep Networks, Journal of Machine Learning for Modeling and Computing, 10.1615/JMachLearnModelComput.2022042093, 2022 Details PDF M. Ahmmed, V.V. Vesselinov, Machine learning and a process model to better characterize hidden geothermal resources, Geothermal Rising Conference (transactions), 2022 Details Link B. Ahmmed, V.V. Vesselinov, Machine learning and shallow groundwater chemistry to identify geothermal prospects in the Great Basin, Renewable Energy, 10.1016/j.renene.2022.08.024, 2022 Details PDF V.V. Vesselinov, M. Mudunuru, B. Ahmmed, S. Karra, D. O'Malley, Machine Learning to Discover, Characterize, and Produce Geothermal Energy, Machine Learning Applications in Subsurface Energy Resource Management, 10.1201/9781003207009-6, 2022 Details Link A. Rupe, V.V. Vesselinov, J.P. Crutchfield, Nonequilibrium statistical mechanics and optimal prediction of partially-observed complex systems, New Journal of Physics, 10.1088/1367-2630/ac95b7, 2022 Details PDF B. Ahmmed, V.V. Vesselinov, M.K. Mudunuru, SmartTensors: Unsupervised and physics-informed machine learning framework for the geoscience applications, Second International Meeting for Applied Geoscience & Energy, 10.1190/image2022-w8-01.1, 2022 Details Link B. Ahmmed, M.K. Mudunuru, S. Karra, S.C. James, V.V. Vesselinov, A comparative study of machine learning models for predicting the state of reactive mixing, Journal of Computational Physics, 10.1016/j.jcp.2021.110147, 2021 Details PDF G. Morra, E. Bozdag, M. Knepley, L. Rass, V.V. Vesselinov, A Tectonic Shift in Analytics and Computing Is Coming, AGU EOS, 10.1029/2021EO159258, 2021 Details PDF S.W. Fleming, V.V. Vesselinov, A.G. Goodbody, Augmenting geophysical interpretation of data-driven operational water supply forecast modeling for a western US river using a hybrid machine learning approach, Journal of Hydrology, 10.1016/j.jhydrol.2021.126327, 2021 Details PDF V.V. Vesselinov, B. Ahmmed, M. Mudunuru, S. Karra, R. Middleton, Discovering the Hidden Geothermal Signatures of Southwest New Mexico, World Geothermal Congress, Reykjavik, Iceland, 2021 Details PDF D. O'Malley, J.K. Golden, V.V. Vesselinov, Learning to regularize with a variational autoencoder for hydrologic inverse analysis, arXiv, arXiv:1906.02401v1, 2021 Details PDF S.W. Fleming, J.R. Watson, A. Ellenson, A.J. Cannon, V.V. Vesselinov, Machine learning in Earth and environmental science requires education and research policy reforms, Nature Geoscience, 10.1038/s41561-021-00865-3, 2021 Details PDF B. Ahmmed, M.K. Mudunuru, S. Karra, V.V. Vesselinov, Machine Learning to Discover Mineral Trapping Signatures due to CO2 Injection, International Journal of Greenhouse Gas Control, 10.1016/j.ijggc.2021.103382, 2021 Details PDF D.L. Siler, J.D. Pepin, V.V. Vesselinov, et al., Machine learning to identify geologic factors associated with production in geothermal fields: A case-study using 3D geologic data, Brady geothermal field, Nevada, Geothermal Energy, 10.1186/s40517-021-00199-8, 2021 Details PDF M. Mehana, E. Guiltinan, V.V. Vesselinov, R. Middleton, J. Hyman, Q. Kang, H. Viswanathan, Machine-Learning Predictions of the Shale Wells’ Performance, Journal of Natural Gas Science and Engineering, 10.1016/j.jngse.2021.103819, 2021 Details PDF V.V. Vesselinov, M.K. Mudunuru, B. Ahmmed, S. Karra, R.S. Middleton, Discovering Signatures of Hidden Geothermal Resources based on Unsupervised Learning, Stanford Geothermal Workshop, 2020 Details PDF PDF missing locally (link kept as-is)
M.K. Mudunuru, H.S. Viswanathan, J. W. Carey, L. Chen, Q. Kang, S. Karra, V.V. Vesselinov, R.S. Middleton, P.A. Johnson, Subsurface energy: Flow and reactive-transport in porous and fractured media, Handbook of Porous Materials (invited), World Scientific Publishers, 10.1142/9789811223419_0004, 2020 Details PDF B.S. Alexandrov, V. Stanev, V.V. Vesselinov, K. Rasmussen, Nonnegative tensor decomposition with custom clustering for microphase separation of block copolymers, Statistical Analysis and Data Mining, 10.1002/sam.11407, 2019 Details PDF V.V. Vesselinov, M. Mudunuru, S. Karra, D. O'Malley, B.S. Alexandrov, Unsupervised Machine Learning Based on Non-Negative Tensor Factorization for Analyzing Reactive-Mixing, Journal of Computational Physics, 10.1016/j.jcp.2019.05.039, 2019 Details PDF C.A. Lopez, V.V. Vesselinov, S. Gnanakaran, B.S. Alexandrov, Unsupervised Machine Learning for Analysis of Coexisting Lipid Phases and Domain Growth in Biological Membranes, J. Chem. Theory Comput., 10.1021/acs.jctc.9b00074, 2019 Details PDF S.K. Hansen, J. He, V.V. Vesselinov, Characterizing the impact of model error in hydrologic time series recovery inverse problems, Advances in Water Resources, 10.1017/j.advwatres.2017.146.R2, 2018 Details PDF S.K. Hansen, C.P. Haslauer, O.A. Cirpka, V.V. Vesselinov, Direct Breakthrough Curve Prediction from Statistics of Heterogeneous Conductivity Fields, Water Resources Research, 10.1002/2017WR020450, 2018 Details PDF V.G. Stanev, F.L. Iliev, S.K. Hansen, V.V. Vesselinov, B.S. Alexandrov, Identification of the release sources in advection-diffusion system by machine learning combined with Green function inverse method, Applied Mathematical Modelling, 10.1016/j.apm.2018.03.006, 2018 Details PDF Z. Lu, V.V. Vesselinov, H. Lei, Identifying Arbitrary Parameter Zonation using Multiple Level Set Functions, Journal of Computational Physics, 10.1016/j.jcp.2018.03.016, 2018 Details PDF K. Telfeyan, A.A. Migdisov, S. Pandey, V.V. Vesselinov, P.W. Reimus, Long-term stability of dithionite in alkaline anaerobic aqueous solution, Applied Geochemistry, 10.1016/j.apgeochem.2018.12.015, 2018 Details PDF E. Qian, B. Peherstorfer, D. O'Malley, V.V. Vesselinov, K. Wilcox, Multifidelity Monte Carlo Estimation of Variance and Sensitivity Indices, SIAM Journal on Uncertainty Quantification, 10.1137/17M1151006, 2018 Details PDF F.L. Iliev, V.G. Stanev, V.V. Vesselinov, B.S. Alexandrov, Nonnegative Matrix Factorization for identification of unknown number of sources emitting delayed signals, PLoS ONE, 10.1371/journal.pone.0193974, 2018 Details PDF V.V. Vesselinov, B.S. Alexandrov, D. O'Malley, Nonnegative Tensor Factorization for Contaminant Source Identification, Journal of Contaminant Hydrology, 10.1016/j.jconhyd.2018.11.010, 2018 Details PDF D. O'Malley, V.V. Vesselinov, B.S. Alexandrov, L.B. Alexandrov, Nonnegative/binary matrix factorization with a D-Wave quantum annealer, PlosOne, 10.1371/journal.pone.0206653, 2018 Details PDF PDF missing locally (link kept as-is)
Y. Lin, D. O'Malley, V.V. Vesselinov, G.D Guthrie, D. Coblentz, Randomization in Characterizing the Subsurface, SIAM News, 2018 Details Link V.V. Vesselinov, Karra Mudunuru. M., O'Malley S., Alexandrov D., Unsupervised Machine Learning Based on Non-negative Tensor Factorization for Analysis of Filed Data and Simulation Outputs, Computational Methods in Water Resources (CMWR), Saint-Malo, France, 10.13140/RG.2.2.27777.92005, 2018 Details PDF V. Stanev, V.V. Vesselinov, A.G. Kusne, G. Antoszewski, I. Takeuchi, B.A. Alexandrov, Unsupervised Phase Mapping of X-ray Diffraction Data by Nonnegative Matrix Factorization Integrated with Custom Clustering, Nature Computational Materials, 10.1038/s41524-018-0099-2, 2018 Details PDF J. Bakarji, V.V. Vesselinov, D. O’Malley, Agent-based Socio-hydrological Hybrid Modeling for Water Resource Management, Water Resources Management, 10.1007/s11269-017-1713-7, 2017 Details PDF S.K. Hansen, S. Pandey, S. Karra, V.V. Vesselinov, CHROTRAN 1.0: A mathematical and computational model for in situ heavy metal remediation in heterogeneous aquifers, Geoscientific Model Development, 10.5194/gmd-10-4525-2017, 2017 Details PDF V.V. Vesselinov, D. O'Malley, B.S. Alexandrov, Contaminant source identification using semi-supervised machine learning, Journal of Contaminant Hydrology, 10.1016/j.jconhyd.2017.11.002, 2017 Details PDF S.K. Hansen, V.V. Vesselinov, P. Reimus, Z. Lu, Inferring subsurface heterogeneity from push-drift tracer tests, Water Resources Research, 10.1002/2017WR020852R, 2017 Details PDF X. Zhang, V.V. Vesselinov, Integrated Modeling Approach for Optimal Management of Water, Energy and Food Security Nexus, Advances in Water Resources, 10.1016/j.advwatres.2016.12.017, 2017 Details PDF Y. Lin, E.B. Le, D. O'Malley, V.V. Vesselinov, T. Bui-Thanh, Large-Scale Inverse Model Analyses Employing Fast Randomized Data Reduction, Water Resources Research, 10.1002/2016WR020299RRR, 2017 Details PDF S.K. Hansen, V.V. Vesselinov, Local equilibrium and retardation revisited, Groundwater, 10.1111/gwat.12551, 2017 Details PDF X. Zhang, A.Y. Sun, I.J. Duncan, V.V. Vesselinov, Two-Stage Fracturing Wastewater Management in Shale Gas Development, Ind. Eng. Chem. Res., 10.1021/acs.iecr.6b03971, 2017 Details PDF Y. Lin, D. O'Malley, V.V. Vesselinov, A computationally efficient parallel Levenberg-Marquardt algorithm for highly parameterized inverse model analyses, Water Resources Research, 10.1002/2016WR019028, 2016 Details PDF H. Throckmorton, B. Newman, J. Heikoop, G. Perkins, X. Feng, D. Graham, D. O'Malley, V.V. Vesselinov, J. Young, S. Wullschleger, C. Wilson, Active layer hydrology in an arctic tundra ecosystem: quantifying water sources and cycling using water stable isotopes, Hydrological Processes, 10.1002/hyp.10883, 2016 Details PDF S.K. Hansen, V.V. Vesselinov, Contaminant point source localization error estimates as functions of data quantity and model quality, Journal of Contaminant Hydrology, 10.1016/j.jconhyd.2016.09.003, 2016 Details PDF M. Grasinger, D. O'Malley, V.V. Vesselinov, S. Karra, Decision Analysis for Robust CO2 Injection: Application of Bayesian-Information-Gap Decision Theory, International Journal of Greenhouse Gas Control, 10.1016/j.ijggc.2016.02.017, 2016 Details PDF PDF missing locally (link kept as-is)
X. Zhang, V.V. Vesselinov, Energy-Water Nexus: Balancing the Tradeoffs between Two-Level Decision Makers, Applied Energy, 10.1016/j.apenergy.2016.08.156, 2016 Details PDF S.K. Hansen, B. Berkowitz, V.V. Vesselinov, D. O'Malley, S. Karra, Push-pull tracer tests: their information content and use for characterizing non-Fickian, mobile-immobile behavior, Water Resources Research, 10.1002/2016WR018769RR, 2016 Details PDF D. O'Malley, V.V. Vesselinov, ToQ.jl: A high-level programming language for D-Wave machines based on Julia, IEEE High Performance Extreme Computing, 10.1109/HPEC.2016.7761616, 2016 Details PDF Z. Lu, V.V. Vesselinov, Analytical Sensitivity Analysis of Transient Groundwater Flow in a Bounded Model Domain using Adjoint Method, Water Resources Research, 10.1002/2014WR016819, 2015 Details PDF D. O’Malley, V.V. Vesselinov, Bayesian-Information-Gap Decision Theory (BIG-DT) with an application to CO2 sequestration, Water Resources Research, 10.1002/2015WR017413, 2015 Details PDF D. O’Malley, V.V. Vesselinov, J.H. Cushman, Diffusive mixing and Tsallis entropy, Physical Review E, 10.1103/PhysRevE.91.042143, 2015 Details PDF D. A. Barajas-Solano, B. Wohlberg, V.V. Vesselinov, D. M. Tartakovsky, Linear Functional Minimization for Inverse Modeling, Water Resources Research, 10.1002/2014WR016179, 2015 Details PDF S.A. Mattis, T.D. Dawson Butler, Estep C.N., Vesselinov D., V.V., Parameter estimation and prediction for groundwater contamination based on measure theory, Water Resources Research, 10.1002/2015WR017295, 2015 Details PDF D. O’Malley, V.V. Vesselinov, A combined probabilistic/non-probabilistic decision analysis for contaminant remediation, Journal on Uncertainty Quantification, SIAM/ASA, 10.1137/140965132, 2014 Details PDF V.L. Freedman, X. Chen, S. Finsterle, M. Freshley, I. Gorton, L. Gosink, E. Keating, C. Lansing, Murray C. Moeglein W., G. Pau, E. Porter, S. Purohit, M. Rockhold, K. Schuchardt, C. Sivaramakrishnan, V.V. Vesselinov, S. Waichler, A high-performance workflow system for subsurface simulation, Environmental Modelling & Software, 10.1016/j.envsoft.2014.01.030, 2014 Details PDF D. O’Malley, V.V. Vesselinov, J.H. Cushman, A Method for Identifying Diffusive Trajectories with Stochastic Model, Journal of Statistical Physics, Springer, 10.1007/s10955-014-1035-6, 2014 Details PDF D. O’Malley, V.V. Vesselinov, Analytical solutions for anomalous dispersion transport, Advances in Water Resources, 10.1016/j.advwatres.2014.02.006, 2014 Details PDF B. Alexandrov, V.V. Vesselinov, Blind source separation for groundwater level analysis based on non-negative matrix factorization, Water Resources Research, 10.1002/2013WR015037, 2014 Details PDF D. O’Malley, V.V. Vesselinov, Groundwater remediation using the information gap decision theory, Water Resources Research, 10.1002/2013WR014718, 2014 Details PDF J.M. Heikoop, T.M. Johnson, K.H. Birdsell, P. Longmire, D.D. Hickmott, E.P. Jacobs, D.E. Broxton, D. Katzman, V.V. Vesselinov, M. Ding, D.T. Vaniman, S.L. Reneau, T.J. Goering, J. Glessner, A. Basu, Isotopic evidence for reduction of anthropogenic hexavalent chromium in Los Alamos National Laboratory groundwater, Chemical Geology, 10.1016/j.chemgeo.2014.02.022, 2014 Details PDF V.V. Vesselinov, D. O'Malley, D. Katzman, Robust Decision Analysis for Environmental Management of Groundwater Contamination Sites, Vulnerability, Uncertainty, and Risk Quantification, Mitigation, and Management, 10.1061/9780784413609.197, 2014 Details Link D.R. Harp, V.V. Vesselinov, Accounting for the influence of aquifer heterogeneity on spatial propagation of pumping drawdown, Journal of Water Resource and Hydraulic Engineering, 2013 Details PDF V.V. Vesselinov, D. Katzman, D. Broxton, K. Birdsell, S. Reneau, D. Vaniman, P. Longmire, J. Fabryka-Martin, J. Heikoop, M. Ding, D. Hickmott, E. Jacobs, T. Goering, D.R. Harp, P. Mishra, Data and Model-Driven Decision Support for Environmental Management of a Chromium Plume at Los Alamos National Laboratory (LANL), Waste Management Symposium, Phoenix, AZ, 2013 Details PDF V.V. Vesselinov, D. Harp, Adaptive hybrid optimization strategy for calibration and parameter estimation of physical models, Computers & Geosciences, 10.1016/j.cageo.2012.05.027, 2012 Details PDF V.V. Vesselinov, G. Pau, . Finsterle, AGNI: Coupling Model Analysis Tools and High-Performance Subsurface Flow and Transport Simulators for Risk and Performance Assessments, Computational Methods in Water Resources (CMWR), 2012 Details PDF D. Harp, V.V. Vesselinov, An agent-based approach to global uncertainty and sensitivity analysis, Computers & Geosciences, 10.1016/j.cageo.2011.06.025, 2012 Details PDF D. Harp, V.V. Vesselinov, Analysis of hydrogeological structure uncertainty by estimation of hydrogeological acceptance probability of geostatistical models, Special issue of Uncertainty Quantification (invited), Advances in Water Resources, 10.1016/j.advwatres.2011.06.007, 2012 Details PDF D. Harp, V.V. Vesselinov, Contaminant remediation decision analysis using information gap theory, Stochastic Environmental Research and Risk Assessment (SERRA), 10.1007/s00477-012-0573-1, 2012 Details PDF P.K. Mishra, H.V. Gupta, V.V. Vesselinov, On simulation and analysis of variable-rate pumping tests, Ground Water, 10.1111/j.1745-6584.2012.00961.x, 2012 Details PDF P.K. Mishra, V.V. Vesselinov, S.P. Neuman, Radial flow to a partially penetrating well with storage in an anisotropic confined aquifer, Journal of Hydrology, 10.1016/j.jhydrol.2012.05.010, 2012 Details PDF P.K. Mishra, V.V. Vesselinov, K.L. Kuhlmna, Saturated–unsaturated flow in a compressible leaky-unconfined aquifer, Advances in Water Resources, 10.1016/j.advwatres.2012.03.007, 2012 Details PDF P.K. Mishra, V.V. Vesselinov, Unified Analytical Solution for Radial Flow to a Well in a Confined Aquifer, arXiv, arXiv:1110.5940, 2011 Details PDF V.V. Vesselinov, D. Harp, Decision support based on uncertainty quantification of model predictions of contaminant transport, Computational Methods in Water Resources (CMWR), Barcelona, Spain, 2010 Details PDF D. Harp, V.V. Vesselinov, Identification of Pumping Influences in Long-Term Water Level Fluctuations, Ground Water, 10.1111/j.1745-6584.2010.00725.x, 2010 Details PDF E. Morales-Casique, S.P. Neuman, V.V. Vesselinov, Maximum Likelihood Bayesian Averaging of air flow models in unsaturated fractured tuff using Occam and variance windows, Special issue of Stochastic Environmental Research and Risk Assessment (SERRA) Journal celebrating 70th anniversary of Shlomo P Neuman, 10.1007/s00477-010-0383-2, 2010 Details PDF D. Harp, V.V. Vesselinov, Stochastic inverse method for estimation of geostatistical representation of hydrogeologic stratigraphy using borehole logs and pressure, Special issue of Stochastic Environmental Research and Risk Assessment (SERRA) Journal celebrating 70th anniversary of Shlomo P Neuman, 10.1007/s00477-010-0403-2, 2010 Details PDF V.V. Vesselinov, Uncertainties in Transient Capture-Zone Estimates of Groundwater Supply Wells, Journal of Contemporary Water Research & Education, 10.1111/j.1936-704X.2007.mp137001001.x, 2007 Details Link V.V. Vesselinov, Uncertainties In Transient Capture-Zone Estimates, Computational Methods in Water Resources (CMWR), ISBN 90-5809-124-4, 2006 Details PDF E.H. Keating, B.A. Robinson, V.V. Vesselinov, Development and Application of Numerical Models to Estimate Fluxes through the Regional Aquifer beneath the Pajarito Plateau, Vadose Zone Journal, 10.2136/vzj2004.0101, 2005 Details PDF J.A. Vrugt, B.A. Robinson, V.V. Vesselinov, Improved Inverse Modeling in Geophysics: Combined Parameter and State Estimation, Geophysical Research Letters, 10.1029/2005GL023940, 2005 Details PDF V.V. Vesselinov, Estimation of parameter uncertainty using inverse model sensitivities, Computational Methods in Water Resources (CMWR), Elsevier, ISBN 0-444-51839-8, 2004 Details PDF Keating 2002 model coupling GW PDF Talsma et al 2022 Characterizing Drought Behavior in the Colorado River Basin Using Unsupervised PDF Vesselinov & Neuman 2000 PDF Vesselinov 2004 inverse sensitivities PDF Vesselinov 2006 transient capture zones IAHS304 19 144 PDF Vesselinov and Robinson 2005 Delineation of capture zones in transient groundwater flow systems ModelCARE PDF Vesselinov et al 2001 part 1 PDF Vesselinov et al 2001 part 2 PDF Vesselinov et al 2015 Model Assisted Decision Analyses Related to a Chromium Plume at Los Alamos National Laboratory PDF Vesselinov OMalley Katzman 2016 ZEM Integrated Framework for Real Time Data and Model Analyses for Robust Environmental Management Decision Making PDF Zyvoloski & Vesselinov 2006 grid GW PDF