Velimir "monty" Vesselinov, PhD

Publications

Count: 96

• Machine Learning Estimates of Geothermal and Critical Mineral Prospectivity of the Great Basin (2026) — V.V. Vesselinov, T.L. Kliphuis — Stanford Geothermal Workshop •PDF
• AI-augmented geothermal model for scalable energy uncertainties in buildings (2026) — A. Markowitz et al. — Scientific Reports
• Deep-Embedded-Clustering of Microseismicity Identifies Multiple Failure Mechanisms at The Geysers Geothermal Field (2026) — P. Lara et al. — Geophysics (submitted)
• 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
• GeoDAWN To GeoTGo: From Complex Data To Decisions Related To Geothermal Prospectivity (2025) — T.L. Kliphuis et al. — Stanford Geothermal Workshop, Stanford, CA •PDF
• 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 •PDF
• 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 •PDF
• DPFEHM: a differentiable subsurface physics simulator (2023) — D. O'Malley et al. — Journal of Open Source Software
• Machine Learning for Geothermal Resource Exploration in Tularosa Basin, New Mexico (2023) — M. Mudunuru et al. — Energies •PDF
• 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 •PDF
• 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 •PDF
• 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
• 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
• Discovering hidden geothermal signatures using non-negative matrix factorization with customized k-means clustering (2022) — V.V. Vesselinov et al. — Geothermics •PDF
• GeoThermalCloud: Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources (2022) — V.V. Vesselinov et al. — Stanford Geothermal Workshop, Stanford, CA •PDF
• GeoThermalCloud: Machine learning for geothermal resource exploration (2022) — M. Mudunuru, V.V. Vesselinov, B. Ahmmed — Journal of Machine Learning for Modeling and Computing •PDF
• 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 •PDF
• Machine learning and a process model to better characterize hidden geothermal resources (2022) — M. Ahmmed, V.V. Vesselinov — Geothermal Rising Conference (transactions) •PDF
• Machine learning and shallow groundwater chemistry to identify geothermal prospects in the Great Basin (2022) — B. Ahmmed, V.V. Vesselinov — Renewable Energy •PDF
• Machine Learning to Discover, Characterize, and Produce Geothermal Energy (2022) — V.V. Vesselinov et al. — Machine Learning Applications in Subsurface Energy Resource Management
• Nonequilibrium statistical mechanics and optimal prediction of partially-observed complex systems (2022) — A. Rupe, V.V. Vesselinov, J.P. Crutchfield — New Journal of Physics •PDF
• 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
• A comparative study of machine learning models for predicting the state of reactive mixing (2021) — B. Ahmmed et al. — Journal of Computational Physics •PDF
• A Tectonic Shift in Analytics and Computing Is Coming (2021) — G. Morra et al. — AGU EOS •PDF
• 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 •PDF
• Discovering the Hidden Geothermal Signatures of Southwest New Mexico (2021) — V.V. Vesselinov et al. — World Geothermal Congress, Reykjavik, Iceland •PDF
• Learning to regularize with a variational autoencoder for hydrologic inverse analysis (2021) — D. O'Malley, J.K. Golden, V.V. Vesselinov — arXiv •PDF
• Machine learning in Earth and environmental science requires education and research policy reforms (2021) — S.W. Fleming et al. — Nature Geoscience •PDF
• Machine Learning to Discover Mineral Trapping Signatures due to CO2 Injection (2021) — B. Ahmmed et al. — International Journal of Greenhouse Gas Control •PDF
• 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 •PDF
• Machine-Learning Predictions of the Shale Wells’ Performance (2021) — M. Mehana et al. — Journal of Natural Gas Science and Engineering •PDF
• Discovering Signatures of Hidden Geothermal Resources based on Unsupervised Learning (2020) — V.V. Vesselinov et al. — Stanford Geothermal Workshop •PDF
• 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 •PDF
• Nonnegative tensor decomposition with custom clustering for microphase separation of block copolymers (2019) — B.S. Alexandrov et al. — Statistical Analysis and Data Mining •PDF
• Unsupervised Machine Learning Based on Non-Negative Tensor Factorization for Analyzing Reactive-Mixing (2019) — V.V. Vesselinov et al. — Journal of Computational Physics •PDF
• 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. •PDF
• 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 •PDF
• Direct Breakthrough Curve Prediction from Statistics of Heterogeneous Conductivity Fields (2018) — S.K. Hansen et al. — Water Resources Research •PDF
• 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 •PDF
• Identifying Arbitrary Parameter Zonation using Multiple Level Set Functions (2018) — Z. Lu, V.V. Vesselinov, H. Lei — Journal of Computational Physics •PDF
• Long-term stability of dithionite in alkaline anaerobic aqueous solution (2018) — K. Telfeyan et al. — Applied Geochemistry •PDF
• Multifidelity Monte Carlo Estimation of Variance and Sensitivity Indices (2018) — E. Qian et al. — SIAM Journal on Uncertainty Quantification •PDF
• Nonnegative Matrix Factorization for identification of unknown number of sources emitting delayed signals (2018) — F.L. Iliev et al. — PLoS ONE •PDF
• Nonnegative Tensor Factorization for Contaminant Source Identification (2018) — V.V. Vesselinov, B.S. Alexandrov, D. O'Malley — Journal of Contaminant Hydrology •PDF
• Nonnegative/binary matrix factorization with a D-Wave quantum annealer (2018) — D. O'Malley et al. — PlosOne •PDF
• Randomization in Characterizing the Subsurface (2018) — Y. Lin et al. — SIAM News •PDF
• 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 •PDF
• Unsupervised Phase Mapping of X-ray Diffraction Data by Nonnegative Matrix Factorization Integrated with Custom Clustering (2018) — V. Stanev et al. — Nature Computational Materials •PDF
• Agent-based Socio-hydrological Hybrid Modeling for Water Resource Management (2017) — J. Bakarji, V.V. Vesselinov, D. O’Malley — Water Resources Management •PDF
• 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 •PDF
• Contaminant source identification using semi-supervised machine learning (2017) — V.V. Vesselinov, D. O'Malley, B.S. Alexandrov — Journal of Contaminant Hydrology •PDF
• Inferring subsurface heterogeneity from push-drift tracer tests (2017) — S.K. Hansen et al. — Water Resources Research •PDF
• Integrated Modeling Approach for Optimal Management of Water, Energy and Food Security Nexus (2017) — X. Zhang, V.V. Vesselinov — Advances in Water Resources •PDF
• Large-Scale Inverse Model Analyses Employing Fast Randomized Data Reduction (2017) — Y. Lin et al. — Water Resources Research •PDF
• Local equilibrium and retardation revisited (2017) — S.K. Hansen, V.V. Vesselinov — Groundwater •PDF
• Two-Stage Fracturing Wastewater Management in Shale Gas Development (2017) — X. Zhang et al. — Ind. Eng. Chem. Res. •PDF
• 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 •PDF
• Active layer hydrology in an arctic tundra ecosystem: quantifying water sources and cycling using water stable isotopes (2016) — H. Throckmorton et al. — Hydrological Processes •PDF
• 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 •PDF
• Decision Analysis for Robust CO2 Injection: Application of Bayesian-Information-Gap Decision Theory (2016) — M. Grasinger et al. — International Journal of Greenhouse Gas Control •PDF
• Energy-Water Nexus: Balancing the Tradeoffs between Two-Level Decision Makers (2016) — X. Zhang, V.V. Vesselinov — Applied Energy •PDF
• 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 •PDF
• 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 •PDF
• Analytical Sensitivity Analysis of Transient Groundwater Flow in a Bounded Model Domain using Adjoint Method (2015) — Z. Lu, V.V. Vesselinov — Water Resources Research •PDF
• Bayesian-Information-Gap Decision Theory (BIG-DT) with an application to CO2 sequestration (2015) — D. O’Malley, V.V. Vesselinov — Water Resources Research •PDF
• Diffusive mixing and Tsallis entropy (2015) — D. O’Malley, V.V. Vesselinov, J.H. Cushman — Physical Review E •PDF
• Linear Functional Minimization for Inverse Modeling (2015) — D. A. Barajas-Solano et al. — Water Resources Research •PDF
• Parameter estimation and prediction for groundwater contamination based on measure theory (2015) — S.A. Mattis et al. — Water Resources Research •PDF
• A combined probabilistic/non-probabilistic decision analysis for contaminant remediation (2014) — D. O’Malley, V.V. Vesselinov — Journal on Uncertainty Quantification, SIAM/ASA •PDF
• A high-performance workflow system for subsurface simulation (2014) — V.L. Freedman et al. — Environmental Modelling & Software •PDF
• A Method for Identifying Diffusive Trajectories with Stochastic Model (2014) — D. O’Malley, V.V. Vesselinov, J.H. Cushman — Journal of Statistical Physics, Springer •PDF
• Analytical solutions for anomalous dispersion transport (2014) — D. O’Malley, V.V. Vesselinov — Advances in Water Resources •PDF
• Blind source separation for groundwater level analysis based on non-negative matrix factorization (2014) — B. Alexandrov, V.V. Vesselinov — Water Resources Research •PDF
• Groundwater remediation using the information gap decision theory (2014) — D. O’Malley, V.V. Vesselinov — Water Resources Research •PDF
• Isotopic evidence for reduction of anthropogenic hexavalent chromium in Los Alamos National Laboratory groundwater (2014) — J.M. Heikoop et al. — Chemical Geology •PDF
• 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 •PDF
• 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 •PDF
• 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 •PDF
• Adaptive hybrid optimization strategy for calibration and parameter estimation of physical models (2012) — V.V. Vesselinov, D. Harp — Computers & Geosciences •PDF
• 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) •PDF
• An agent-based approach to global uncertainty and sensitivity analysis (2012) — D. Harp, V.V. Vesselinov — Computers & Geosciences •PDF
• 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 •PDF
• Contaminant remediation decision analysis using information gap theory (2012) — D. Harp, V.V. Vesselinov — Stochastic Environmental Research and Risk Assessment (SERRA) •PDF
• On simulation and analysis of variable-rate pumping tests (2012) — P.K. Mishra, H.V. Gupta, V.V. Vesselinov — Ground Water •PDF
• 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 •PDF
• Saturated–unsaturated flow in a compressible leaky-unconfined aquifer (2012) — P.K. Mishra, V.V. Vesselinov, K.L. Kuhlmna — Advances in Water Resources •PDF
• Unified Analytical Solution for Radial Flow to a Well in a Confined Aquifer (2011) — P.K. Mishra, V.V. Vesselinov — arXiv •PDF
• 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 •PDF
• Identification of Pumping Influences in Long-Term Water Level Fluctuations (2010) — D. Harp, V.V. Vesselinov — Ground Water •PDF
• 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 •PDF
• 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 •PDF
• Uncertainties in Transient Capture-Zone Estimates of Groundwater Supply Wells (2007) — V.V. Vesselinov — Journal of Contemporary Water Research & Education
• Uncertainties In Transient Capture-Zone Estimates (2006) — V.V. Vesselinov — Computational Methods in Water Resources (CMWR) •PDF
• 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 •PDF
• Improved Inverse Modeling in Geophysics: Combined Parameter and State Estimation (2005) — J.A. Vrugt, B.A. Robinson, V.V. Vesselinov — Geophysical Research Letters •PDF
• Estimation of parameter uncertainty using inverse model sensitivities (2004) — V.V. Vesselinov — Computational Methods in Water Resources (CMWR), Elsevier •PDF

See more publications montyv.github.io.

Presentations & Lectures

Count: 74

• Science-informed machine learning for reservoir characterization (2026) — Computational Methods in Water Resources (CMWR), Bologna, Italy — V.V. Vesselinov, T.L. Kliphuis
• BEYOND PROSPECTIVITY MAPS: Multimodal Geospatial AI for Critical Mineral Systems and Geothermal Prospectivity Exploration (2026) — ML4SEG (Machine Learning for Solid Earth Geosciences and Earthquake Physics), Sicily, Italy — V.V. Vesselinov, T.L. Kliphuis
• Imaging Subsurface Reservoirs for Hydrogen, Geothermal, and Critical Minerals: Multi-Physics Imaging and AI-Assisted Interpretation (2026) — AI for Geological Mapping and Modelling Conference (AIGMM), Utrecht, Netherlands — V.V. Vesselinov, T.L. Kliphuis
• Machine Learning Estimates of Geothermal and Critical Mineral Prospectivity of the Great Basin (2026) — Stanford Geothermal Workshop — V.V. Vesselinov, T.L. Kliphuis •DOI
• Machine Learning Estimates of Geothermal and Critical Mineral Prospectivity of the Great Basin (2026) — Stanford Geothermal Workshop — V.V. Vesselinov, T. Kliphuis •DOI
• GAIA: Cloud Framework for Geospatial Artificial Intelligence Analyses (2025) — HydroML 2025, University of California, Irvine — T. Kliphuis, V.V. Vesselinov •DOI
• GeoDAWN & GeoFLIGHT to GeoTGo: From complex data to defensible decisions related to geothermal prospectivity (2025) — Stanford Geothermal Workshop — V.V. Vesselinov et al. •DOI
• UrbanAI Transforming Urban Energy Planning Using Artificial Intelligence (2025) — Machine Learning in Solid Earth Geoscience — R. Yang et al. •DOI
• When Earth Meets AI (2025) — RealmIQ El Sailon — V.V. Vesselinov, T. Kliphuis
• Laying the groundwork for a greener future: ML for characterizing and managing geologic reservoirs (2024) — V.V. Vesselinov, H. Jasperson, T. Kliphuis
• Mapping geothermal resources using AI/ML (2024) — New Mexico Geological Society Annual Spring Meeting — T. Kliphuis, H. Jasperson, V.V. Vesselinov •DOI
• GeoTGO: Equitable and Inclusive Tool for Community-Based Geothermal (2023) — Stanford Geothermal Workshop — T. Kliphuis, M. Bluehouse, V.V. Vesselinov
• ChemML: Understanding groundwater flow and contaminant transport using machine learning (2022) — American Geophysical Union, San Juan, Puerto Rico — V.V. Vesselinov, T. Kliphuis
• Machine learning and a process model to better characterize hidden geothermal resources (2022) — Geothermal Rising Conference (GRC) — M. Ahmmed, V.V. Vesselinov
• Machine Learning for Small, Uncertain, and Sparse Data Sets (2022) — AGU Fall Meeting — M. Ahmmed, V.V. Vesselinov
• Novel machine learning methods and tools for geothermal and geochemical problems (2022) — AGU Fall Meeting — V.V. Vesselinov, T. Kliphuis
• Physics-Informed Machine Learning of Geothermal, Geomechanical, Geochemical Process (2022) — AGU Fall Meeting — V.V. Vesselinov, T. Kliphuis
• Practical Glass-Box Machine Learning for Seasonal Water Supply Forecasting, with Applications to the Owyhee and Yellowstone Rivers: Regression Using Climate Indices Derived from SNOTEL Data Using Nonnegative Matrix Factorization with k-Means Clustering (2022) — AGU Fall Meeting — W. Fleming, V.V. Vesselinov, A. Goodbody
• Toward Automated Data-Model Calibration for the Arctic Terrestrial Ecosystem Model (2022) — AGU Fall Meeting — E. Jafarov et al.
• GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration and Development of Hidden Geothermal Resources (2021) — Department of Energy, Geothermal Office — V.V. Vesselinov, et al.
• GeoThermalCloud: Fusion of Big Data and Multi-Physics Models (2021) — JuliaCon, Boston, MA — V.V. Vesselinov, et al.
• Hidden geothermal signatures of the southwest New Mexico (2021) — World Geothermal Congress, Reykjavik, Iceland — V.V. Vesselinov, et al.
• Machine Learning to Characterize the State of Stress and its Influence on Geothermal Production (2021) — Geothermal Rising Conference — V.V. Vesselinov, et al.
• ML4Geo: Machine Learning for Geosciences (2021) — JuliaCon, Boston, MA — V.V. Vesselinov, et al.
• SmartTensors: Unsupervised Machine Learning (2021) — JuliaCon, Boston, MA — V.V. Vesselinov, et al.
• Unsupervised and Physics-Informed Machine learning in Geosciences (2021) — Baylor University, Texas — V.V. Vesselinov, et al.
• Discovering Hidden Geothermal Signatures using Unsupervised Machine Learning (2020) — Stanford Geothermal Workshop — V.V. Vesselinov, et al.
• Machine learning for geothermal resource analysis and exploration (2020) — XXIII International Conference on Computational Methods in Water Resources (CMWR), Stanford, CA — V.V. Vesselinov, et al.
• Predicting oil and gas production from unconventional tight-rock reservoirs using machine learning (2020) — XXIII International Conference on Computational Methods in Water Resources (CMWR) — V.V. Vesselinov
• Site-Scale and Regional-Scale Modeling for Geothermal Resource Analysis and Exploration (2020) — Geothermal Workshop, Stanford, CA — M. Mudunuru, V.V. Vesselinov, et al.
• Unsupervised and Physics-Informed Machine Learning Analyses for Characterization of Energy Production from Unconventional Reservoirs (2020) — Machine Learning in Oil & Gas Conference — V.V. Vesselinov
• Unsupervised and Physics-Informed Machine Learning of Big and Noisy Data (2020) — Bureau of Economic Geology, University of Austin, Texas — V.V. Vesselinov
• Machine learning analyses for characterization of oil, gas and water production from unconventional tight-rock reservoirs (2019) — AGU Fall Meeting — V.V. Vesselinov
• Machine Learning Analyses of Climate Data and Models (2019) — 11th World Congress of European Water Resources Association (EWRA), Madrid, Spain — V.V. Vesselinov
• Novel Unsupervised Machine Learning Methods for Data Analytics and Model Diagnostics (2019) — Machine Learning in Solid Earth Geoscience, Santa Fe — V.V. Vesselinov
• Physics-Informed Machine Learning Methods for Data Analytics and Model Diagnostics (2019) — M3 NASA DRIVE Workshop, Los Alamos — V.V. Vesselinov
• Unsupervised and Physics-Informed Machine Learning of Big Data (2019) — Workshop: Applications of Big Data and High-Performance Computing in Earth Sciences, AGU Fall Meeting, San Francisco, CA (invited) — V.V. Vesselinov
• Unsupervised Machine Learning Methods for Feature Extraction (2019) — New Mexico Big Data & Analytics Summit — V.V. Vesselinov
• Unsupervised Machine Learning: Nonnegative Matrix Tensor Decompositions (2019) — MIT, Boston, MA — V.V. Vesselinov
• Novel Machine Learning Methods for Extraction of Features Characterizing Complex Datasets and Models (2018) — Recent Advances in Machine Learning and Computational Methods for Geoscience, Institute for Mathematics and its Applications, University of Minnesota — V.V. Vesselinov •DOI
• Novel Machine Learning Methods for Extraction of Features Characterizing Datasets and Models (2018) — AGU Fall meeting — V.V. Vesselinov
• Novel Robust Machine Learning Methods for Identification and Extraction of Unknown Features in Complex Real-world Data Sets (2018) — Society for Industrial and Applied Mathematics (SIAM) Uncertainty Quantification, Garden Grove, CA (invited) — V.V. Vesselinov, D. O'Malley, B. Alexandrov
• Unsupervised Machine Learning Based on Non-negative Tensor Factorization for Analysis of Field Data and Simulation Outputs (2018) — Computational Methods in Water Resources (CMWR), Saint-Malo, France — V.V. Vesselinov et al. •DOI
• Unsupervised Machine Learning based on Nonnegative Matrix/Tensor Factorization (2018) — World Congress on Computational Mechanics (WCCM) (invited) — V.V. Vesselinov et al.
• Unsupervised Machine Learning Based on Tensor Factorization (2018) — International Society for Porous Media (INTERPORE) — V.V. Vesselinov, D. O'Malley, B. Alexandrov •DOI
• Decision Analyses for Groundwater Remediation (2017) — Waste Management Symposium — V.V. Vesselinov, D. O'Malley, D. Katzman •DOI
• Hydraulic Inverse Modeling using Total-Variation Regularization with Relaxed Variable-Splitting (2017) — SIAM Conference on Computational Science and Engineering — Y. Lin et al.
• Nonnegative/binary matrix factorization with a D-Wave quantum annealer (2017) — DOE LANL Presentation — D. O'Malley et al.
• Quo vadis: Hydrologic inverse analyses using high-performance computing and a D-Wave quantum annealer (2017) — AGU Fall Meeting — D. O'Malley, V.V. Vesselinov •DOI
• Uncertainty quantification and experimental design based on unsupervised machine learning identification of contaminant sources and groundwater types using hydrogeochemical data (2017) — AGU Fall Meeting — V.V. Vesselinov, D. O'Malley, B. Alexandrov
• Analysis of Hydrologic Time Series Reconstruction Uncertainty due to Inverse Model Inadequacy (2016) — AGU Fall Meeting — J. He, S. Hansen, V.V. Vesselinov
• Bi-Level Decision Making for Supporting Energy and Water Nexus (2016) — AGU Fall Meeting — X. Zhang, V.V. Vesselinov
• Groundwater Remediation using Bayesian Information-Gap Decision Theory (2016) — AGU Fall Meeting — D. O'Malley, V.V. Vesselinov
• Hydraulic Inverse Modeling using Total-Variation Regularization with Relaxed Variable-Splitting (2016) — AGU Fall Meeting — Y. Lin et al.
• Identifying Aquifer Heterogeneities using the Level Set Method (2016) — AGU Fall Meeting — Z. Lu, V.V. Vesselinov, H. Lei
• Model Analyses of Complex Systems Behavior using MADS (2016) — AGU Fall Meeting — V.V. Vesselinov, D. O'Malley •DOI
• Prediction of Breakthrough Curves for Conservative and Reactive Transport (2016) — AGU Fall Meeting — S. Hansen et al.
• Reduced Order Models for Decision Analysis and Upscaling of Aquifer Heterogeneity (2016) — AGU Fall Meeting (invited) — V.V. Vesselinov, D. O'Malley, B. Alexandrov
• ZEM: Integrated Framework for Real-Time Data and Model Analyses for Robust Environmental Management Decision Making (2016) — Waste Management Symposium — V.V. Vesselinov, D. O'Malley, D. Katzman •DOI
• Model-Assisted Decision Analyses Related to a Chromium Plume at Los Alamos National Laboratory (2015) — Waste Management Symposium — V.V. Vesselinov, D. O'Malley, D. Katzman •DOI
• A Social Dynamics Dependent Water Supply Well Contamination Model (2014) — DOE LANL Presentation — J. Bakarji, D. O'Malley, V.V. Vesselinov •DOI
• Bayesian Information-Gap Decision Analysis Applied to a Geologic CO2 Sequestration Problem (2014) — AGU Fall Meeting — D. O'Malley, V.V. Vesselinov
• Model-free Source Identification (2014) — AGU Fall Meeting — V.V. Vesselinov, B. Alexandrov
• Random dispersion coefficients and Tsallis entropy (2014) — AGU Fall Meeting — J. Cushman, V.V. Vesselinov, D. O'Malley
• Data and Model-Driven Decision Support for Environmental Management of a Chromium Plume at Los Alamos National Laboratory (LANL) (2013) — Waste Management Symposium — V.V. Vesselinov et al. •DOI
• What Matters When and Where For Anomalous Dispersion/Diffusion (2013) — AGU Fall Meeting — D. O'Malley, V.V. Vesselinov
• AGNI: Coupling Model Analysis Tools and High-Performance Subsurface Flow and Transport Simulators for Risk and Performance Assessments (2012) — XIX International Conference on Computational Methods in Water Resources (CMWR 2012) — V.V. Vesselinov, et al.
• MADS & ASCEM (2012) — AGU Fall Meeting — V.V. Vesselinov
• Model-driven decision support for monitoring network design based on analysis of data and model uncertainties: methods and applications (2012) — AGU Fall meeting (invited) — V.V. Vesselinov, D. Harp, D. Katzman •DOI
• Numerical Optimization using the Levenberg-Marquardt Algorithm (2011) — DOE LANL Presentations — L. Leif Zinn-Bjorkman, V.V. Vesselinov •DOI
• Recent developments in MADS algorithms: ABAGUS and Squads (2011) — DOE LANL Presentations — D. Harp, V.V. Vesselinov •DOI
• Environmental Management Modeling Activities at Los Alamos National Laboratory (LANL) (2010) — Department of Energy Technical Exchange Meeting, Performance Assessment Community of Practice, Hanford — V.V. Vesselinov, et al. •DOI
• Tomographic inverse estimation of aquifer properties based on pressure variations caused by transient water-supply pumping (2008) — AGU Meeting — V.V. Vesselinov et al. •DOI
• Uncertainties in Transient Capture-Zone Estimates (2006) — Conference on Computational Methods in Water Resources (CMWR), Copenhagen, Denmark — V.V. Vesselinov