Publications

Curated Monty publications: 95

• AI-augmented geothermal model for scalable energy uncertainties in buildings (2026) — A. Markowitz et al. — Scientific Reports •DOI
• Deep-Embedded-Clustering of Microseismicity Identifies Multiple Failure Mechanisms at The Geysers Geothermal Field (2026) — P. Lara et al. — Geophysics (submitted) •DOI
• Estimation of above- and below-ground ecosystem parameters for DVM-DOS-TEM v0.7.0 using MADS v1.7.3 (2025) — E.E. Jafarov et al. — Geoscientific Model Development •DOI
• GeoDAWN To GeoTGo: From Complex Data To Decisions Related To Geothermal Prospectivity (2025) — T.L. Kliphuis et al. — Stanford Geothermal Workshop, Stanford, CA •Link
• Machine-Learning Methods and Tools Designed for Community-Based Equitable and Inclusive Geothermal Development (2024) — V.V. Vesselinov, H. Jasperson, T.L. Kliphuis — Stanford Geothermal Workshop, Stanford, CA •Link
• ChemML: Physics-Informed AI/ML of Geochemical Datasets for Characterization, Parameterization, and Prediction of Contaminant Transport and Remediation Processes (2023) — V.V. Vesselinov, T.L. Kliphuis — DOE •Link
• DPFEHM: a differentiable subsurface physics simulator (2023) — D. O'Malley et al. — Journal of Open Source Software •DOI
• Machine Learning for Geothermal Resource Exploration in Tularosa Basin, New Mexico (2023) — M. Mudunuru et al. — Energies •Link
• The drivers and predictability of wildfire re-burns in the western United States (US) (2023) — K.C. Solander, C.J. Talsma, V.V. Vesselinov — Environ. Res.: Climate 2 •Link
• A Progress Report on GeoThermalCloud: Framework An Open-Source Machine Learning Based Tool for Discovery, Exploration, and Development of Hidden Geothermal Resources (2022) — B. Ahmmed et al. — Stanford Geothermal Workshop, Stanford, CA •Link
• Beam Me Up SCO2TPRO: A Comparison to the FE/NETL CO2 Saline Storage Cost Model and Updates on Tool Development (2022) — J. Bennett et al. — SSRN Electronic Journal •DOI
• Characterizing Drought Behavior in the Colorado River Basin Using Unsupervised Machine Learning (2022) — C.J. Talsma, K.E. Bennett, V.V. Vesselinov — Earth and Space Science •DOI
• Discovering hidden geothermal signatures using non-negative matrix factorization with customized k-means clustering (2022) — V.V. Vesselinov et al. — Geothermics •Link
• GeoThermalCloud: Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources (2022) — V.V. Vesselinov et al. — Stanford Geothermal Workshop, Stanford, CA •Link
• GeoThermalCloud: Machine learning for geothermal resource exploration (2022) — M. Mudunuru, V.V. Vesselinov, B. Ahmmed — Journal of Machine Learning for Modeling and Computing •Link
• Inverse Analysis with Variational Autoencoders: A Comparison of Shallow and Deep Networks (2022) — H. Wu et al. — Journal of Machine Learning for Modeling and Computing •Link
• Machine learning and a process model to better characterize hidden geothermal resources (2022) — M. Ahmmed, V.V. Vesselinov — Geothermal Rising Conference (transactions) •Link
• Machine learning and shallow groundwater chemistry to identify geothermal prospects in the Great Basin (2022) — B. Ahmmed, V.V. Vesselinov — Renewable Energy •Link
• Machine Learning to Discover, Characterize, and Produce Geothermal Energy (2022) — V.V. Vesselinov et al. — Machine Learning Applications in Subsurface Energy Resource Management •DOI
• Nonequilibrium statistical mechanics and optimal prediction of partially-observed complex systems (2022) — A. Rupe, V.V. Vesselinov, J.P. Crutchfield — New Journal of Physics •Link
• SmartTensors: Unsupervised and physics-informed machine learning framework for the geoscience applications (2022) — B. Ahmmed, V.V. Vesselinov, M.K. Mudunuru — Second International Meeting for Applied Geoscience & Energy •DOI
• A comparative study of machine learning models for predicting the state of reactive mixing (2021) — B. Ahmmed et al. — Journal of Computational Physics •Link
• A Tectonic Shift in Analytics and Computing Is Coming (2021) — G. Morra et al. — AGU EOS •Link
• Augmenting geophysical interpretation of data-driven operational water supply forecast modeling for a western US river using a hybrid machine learning approach (2021) — S.W. Fleming, V.V. Vesselinov, A.G. Goodbody — Journal of Hydrology •Link
• Discovering the Hidden Geothermal Signatures of Southwest New Mexico (2021) — V.V. Vesselinov et al. — World Geothermal Congress, Reykjavik, Iceland •Link
• Learning to regularize with a variational autoencoder for hydrologic inverse analysis (2021) — D. O'Malley, J.K. Golden, V.V. Vesselinov — arXiv •Link
• Machine learning in Earth and environmental science requires education and research policy reforms (2021) — S.W. Fleming et al. — Nature Geoscience •Link
• Machine Learning to Discover Mineral Trapping Signatures due to CO2 Injection (2021) — B. Ahmmed et al. — International Journal of Greenhouse Gas Control •Link
• Machine learning to identify geologic factors associated with production in geothermal fields: A case-study using 3D geologic data, Brady geothermal field, Nevada (2021) — D.L. Siler, J.D. Pepin, V.V. Vesselinov, et al. — Geothermal Energy •Link
• Machine-Learning Predictions of the Shale Wells’ Performance (2021) — M. Mehana et al. — Journal of Natural Gas Science and Engineering •Link
• Discovering Signatures of Hidden Geothermal Resources based on Unsupervised Learning (2020) — V.V. Vesselinov et al. — Stanford Geothermal Workshop •Link
• Subsurface energy: Flow and reactive-transport in porous and fractured media (2020) — M.K. Mudunuru et al. — Handbook of Porous Materials (invited), World Scientific Publishers •Link
• Nonnegative tensor decomposition with custom clustering for microphase separation of block copolymers (2019) — B.S. Alexandrov et al. — Statistical Analysis and Data Mining •Link
• Unsupervised Machine Learning Based on Non-Negative Tensor Factorization for Analyzing Reactive-Mixing (2019) — V.V. Vesselinov et al. — Journal of Computational Physics •Link
• Unsupervised Machine Learning for Analysis of Coexisting Lipid Phases and Domain Growth in Biological Membranes (2019) — C.A. Lopez et al. — J. Chem. Theory Comput. •Link
• Characterizing the impact of model error in hydrologic time series recovery inverse problems (2018) — S.K. Hansen, J. He, V.V. Vesselinov — Advances in Water Resources •Link
• Direct Breakthrough Curve Prediction from Statistics of Heterogeneous Conductivity Fields (2018) — S.K. Hansen et al. — Water Resources Research •Link
• Identification of the release sources in advection-diffusion system by machine learning combined with Green function inverse method (2018) — V.G. Stanev et al. — Applied Mathematical Modelling •Link
• Identifying Arbitrary Parameter Zonation using Multiple Level Set Functions (2018) — Z. Lu, V.V. Vesselinov, H. Lei — Journal of Computational Physics •Link
• Long-term stability of dithionite in alkaline anaerobic aqueous solution (2018) — K. Telfeyan et al. — Applied Geochemistry •Link
• Multifidelity Monte Carlo Estimation of Variance and Sensitivity Indices (2018) — E. Qian et al. — SIAM Journal on Uncertainty Quantification •Link
• Nonnegative Matrix Factorization for identification of unknown number of sources emitting delayed signals (2018) — F.L. Iliev et al. — PLoS ONE •Link
• Nonnegative Tensor Factorization for Contaminant Source Identification (2018) — V.V. Vesselinov, B.S. Alexandrov, D. O'Malley — Journal of Contaminant Hydrology •Link
• Nonnegative/binary matrix factorization with a D-Wave quantum annealer (2018) — D. O'Malley et al. — PlosOne •Link
• Randomization in Characterizing the Subsurface (2018) — Y. Lin et al. — SIAM News •Link
• Unsupervised Machine Learning Based on Non-negative Tensor Factorization for Analysis of Filed Data and Simulation Outputs (2018) — V.V. Vesselinov et al. — Computational Methods in Water Resources (CMWR), Saint-Malo, France •Link
• Unsupervised Phase Mapping of X-ray Diffraction Data by Nonnegative Matrix Factorization Integrated with Custom Clustering (2018) — V. Stanev et al. — Nature Computational Materials •Link
• Agent-based Socio-hydrological Hybrid Modeling for Water Resource Management (2017) — J. Bakarji, V.V. Vesselinov, D. O’Malley — Water Resources Management •Link
• CHROTRAN 1.0: A mathematical and computational model for in situ heavy metal remediation in heterogeneous aquifers (2017) — S.K. Hansen et al. — Geoscientific Model Development •Link
• Contaminant source identification using semi-supervised machine learning (2017) — V.V. Vesselinov, D. O'Malley, B.S. Alexandrov — Journal of Contaminant Hydrology •Link
• Inferring subsurface heterogeneity from push-drift tracer tests (2017) — S.K. Hansen et al. — Water Resources Research •Link
• Integrated Modeling Approach for Optimal Management of Water, Energy and Food Security Nexus (2017) — X. Zhang, V.V. Vesselinov — Advances in Water Resources •Link
• Large-Scale Inverse Model Analyses Employing Fast Randomized Data Reduction (2017) — Y. Lin et al. — Water Resources Research •Link
• Local equilibrium and retardation revisited (2017) — S.K. Hansen, V.V. Vesselinov — Groundwater •Link
• Two-Stage Fracturing Wastewater Management in Shale Gas Development (2017) — X. Zhang et al. — Ind. Eng. Chem. Res. •Link
• A computationally efficient parallel Levenberg-Marquardt algorithm for highly parameterized inverse model analyses (2016) — Y. Lin, D. O'Malley, V.V. Vesselinov — Water Resources Research •Link
• Active layer hydrology in an arctic tundra ecosystem: quantifying water sources and cycling using water stable isotopes (2016) — H. Throckmorton et al. — Hydrological Processes •Link
• Contaminant point source localization error estimates as functions of data quantity and model quality (2016) — S.K. Hansen, V.V. Vesselinov — Journal of Contaminant Hydrology •Link
• Decision Analysis for Robust CO2 Injection: Application of Bayesian-Information-Gap Decision Theory (2016) — M. Grasinger et al. — International Journal of Greenhouse Gas Control •Link
• Energy-Water Nexus: Balancing the Tradeoffs between Two-Level Decision Makers (2016) — X. Zhang, V.V. Vesselinov — Applied Energy •Link
• Push-pull tracer tests: their information content and use for characterizing non-Fickian, mobile-immobile behavior (2016) — S.K. Hansen et al. — Water Resources Research •Link
• ToQ.jl: A high-level programming language for D-Wave machines based on Julia (2016) — D. O'Malley, V.V. Vesselinov — IEEE High Performance Extreme Computing •Link
• Analytical Sensitivity Analysis of Transient Groundwater Flow in a Bounded Model Domain using Adjoint Method (2015) — Z. Lu, V.V. Vesselinov — Water Resources Research •Link
• Bayesian-Information-Gap Decision Theory (BIG-DT) with an application to CO2 sequestration (2015) — D. O’Malley, V.V. Vesselinov — Water Resources Research •Link
• Diffusive mixing and Tsallis entropy (2015) — D. O’Malley, V.V. Vesselinov, J.H. Cushman — Physical Review E •Link
• Linear Functional Minimization for Inverse Modeling (2015) — D. A. Barajas-Solano et al. — Water Resources Research •Link
• Parameter estimation and prediction for groundwater contamination based on measure theory (2015) — S.A. Mattis et al. — Water Resources Research •Link
• A combined probabilistic/non-probabilistic decision analysis for contaminant remediation (2014) — D. O’Malley, V.V. Vesselinov — Journal on Uncertainty Quantification, SIAM/ASA •Link
• A high-performance workflow system for subsurface simulation (2014) — V.L. Freedman et al. — Environmental Modelling & Software •Link
• A Method for Identifying Diffusive Trajectories with Stochastic Model (2014) — D. O’Malley, V.V. Vesselinov, J.H. Cushman — Journal of Statistical Physics, Springer •Link
• Analytical solutions for anomalous dispersion transport (2014) — D. O’Malley, V.V. Vesselinov — Advances in Water Resources •Link
• Blind source separation for groundwater level analysis based on non-negative matrix factorization (2014) — B. Alexandrov, V.V. Vesselinov — Water Resources Research •Link
• Groundwater remediation using the information gap decision theory (2014) — D. O’Malley, V.V. Vesselinov — Water Resources Research •Link
• Isotopic evidence for reduction of anthropogenic hexavalent chromium in Los Alamos National Laboratory groundwater (2014) — J.M. Heikoop et al. — Chemical Geology •Link
• Robust Decision Analysis for Environmental Management of Groundwater Contamination Sites (2014) — V.V. Vesselinov, D. O'Malley, D. Katzman — Vulnerability, Uncertainty, and Risk Quantification, Mitigation, and Management •Link
• Accounting for the influence of aquifer heterogeneity on spatial propagation of pumping drawdown (2013) — D.R. Harp, V.V. Vesselinov — Journal of Water Resource and Hydraulic Engineering •Link
• Data and Model-Driven Decision Support for Environmental Management of a Chromium Plume at Los Alamos National Laboratory (LANL) (2013) — V.V. Vesselinov et al. — Waste Management Symposium, Phoenix, AZ •Link
• Adaptive hybrid optimization strategy for calibration and parameter estimation of physical models (2012) — V.V. Vesselinov, D. Harp — Computers & Geosciences •Link
• AGNI: Coupling Model Analysis Tools and High-Performance Subsurface Flow and Transport Simulators for Risk and Performance Assessments (2012) — V.V. Vesselinov, G. Pau, . Finsterle — Computational Methods in Water Resources (CMWR) •Link
• An agent-based approach to global uncertainty and sensitivity analysis (2012) — D. Harp, V.V. Vesselinov — Computers & Geosciences •Link
• Analysis of hydrogeological structure uncertainty by estimation of hydrogeological acceptance probability of geostatistical models (2012) — D. Harp, V.V. Vesselinov — Special issue of Uncertainty Quantification (invited), Advances in Water Resources •Link
• Contaminant remediation decision analysis using information gap theory (2012) — D. Harp, V.V. Vesselinov — Stochastic Environmental Research and Risk Assessment (SERRA) •Link
• On simulation and analysis of variable-rate pumping tests (2012) — P.K. Mishra, H.V. Gupta, V.V. Vesselinov — Ground Water •Link
• Radial flow to a partially penetrating well with storage in an anisotropic confined aquifer (2012) — P.K. Mishra, V.V. Vesselinov, S.P. Neuman — Journal of Hydrology •Link
• Saturated–unsaturated flow in a compressible leaky-unconfined aquifer (2012) — P.K. Mishra, V.V. Vesselinov, K.L. Kuhlmna — Advances in Water Resources •Link
• Unified Analytical Solution for Radial Flow to a Well in a Confined Aquifer (2011) — P.K. Mishra, V.V. Vesselinov — arXiv •Link
• Decision support based on uncertainty quantification of model predictions of contaminant transport (2010) — V.V. Vesselinov, D. Harp — Computational Methods in Water Resources (CMWR), Barcelona, Spain •Link
• Identification of Pumping Influences in Long-Term Water Level Fluctuations (2010) — D. Harp, V.V. Vesselinov — Ground Water •Link
• Maximum Likelihood Bayesian Averaging of air flow models in unsaturated fractured tuff using Occam and variance windows (2010) — E. Morales-Casique, S.P. Neuman, V.V. Vesselinov — Special issue of Stochastic Environmental Research and Risk Assessment (SERRA) Journal celebrating 70th anniversary of Shlomo P Neuman •Link
• Stochastic inverse method for estimation of geostatistical representation of hydrogeologic stratigraphy using borehole logs and pressure (2010) — D. Harp, V.V. Vesselinov — Special issue of Stochastic Environmental Research and Risk Assessment (SERRA) Journal celebrating 70th anniversary of Shlomo P Neuman •Link
• Uncertainties in Transient Capture-Zone Estimates of Groundwater Supply Wells (2007) — V.V. Vesselinov — Journal of Contemporary Water Research & Education •DOI
• Uncertainties In Transient Capture-Zone Estimates (2006) — V.V. Vesselinov — Computational Methods in Water Resources (CMWR) — ISBN 90-5809-124-4 •Link
• Development and Application of Numerical Models to Estimate Fluxes through the Regional Aquifer beneath the Pajarito Plateau (2005) — E.H. Keating, B.A. Robinson, V.V. Vesselinov — Vadose Zone Journal •Link
• Improved Inverse Modeling in Geophysics: Combined Parameter and State Estimation (2005) — J.A. Vrugt, B.A. Robinson, V.V. Vesselinov — Geophysical Research Letters •Link
• Estimation of parameter uncertainty using inverse model sensitivities (2004) — V.V. Vesselinov — Computational Methods in Water Resources (CMWR), Elsevier — ISBN 0-444-51839-8 •Link