2022 - present: Co-Founder, CTO, CSO, EnviTrace LLC, Santa Fe, New Mexcio, USA

2022 - present: Founder, CEO, SmartTensors LLC, Santa Fe, New Mexcio, USA

2000 - 2022: Senior Scientist, Los Alamos National Laboratory, Los Alamos, New Mexcio, USA

1995 - 2000: Research Associate, Department of Hydrology and Water Resources, University of Arizona, Tucson, Arizona, USA

1989 - 1995: Associate Professor, Department of Hydrogeology and Engineering Geology, Institute of Mining and Geology, Sofia, Bulgaria

Ph.D., 2000

Department of Hydrology and Water Resources, University of Arizona, Tucson, Arizona, USA
Major: Engineering Hydrology
Minor: Applied Mathematics
Dissertation title: Numerical inverse interpretation of pneumatic tests in unsaturated fractured tuffs at the Apache Leap Research Site
Advisor: Regents Professor Dr. Shlomo P. Neuman

M.Eng., 1989

Department of Hydrogeology and Engineering Geology, Institute of Mining and Geology, Sofia, Bulgaria
Major: Hydrogeology
Minor: Engineering Geology
Dissertation title: Hydrogeological investigation in applying the Vyredox method for groundwater decontamination
Advisor: Professor Dr. Pavel P. Pentchev

• Project management and research leadership
• Machine learning
• Artificial intelligence
• Data analytics
• Model diagnostics
• Risk assessment
• Decision analysis, support and making
• Model simulations and analyes
• Theoretical research
• Code development
• Edge/Cloud/High-performance computing
• Field and experimental work

• Environmental management and restoration
• Carbon sequestration and storage
• Geothermal energy exploration and production
• Oil/gas production
• Climate and anthropogenic impacts
• Wildfires
• Water supply
• Water contamination
• Watershed hydrology
• Induced seismicity
• Waste disposal

• machine learning; unsupervised/supervised/physics-informed; feature extraction; source identification; blind source separation; big-data analytics; real-time data processing;
• high-performance computing; cloud computing, quantum computing;
• evaluation of information content; data-worth analyses; extraction of actionable knowledge/information; decision analyses;
• model analyses; parameter estimation; sensitivity analyses; uncertainty quantification; risk assessment; decision support;
• model abstraction; model reduction; reduced order modeling; model selection; model ranking;
• optimal experimental design; design of experiments, optimal data acquisition;
• General Information Theory (GIT); Fuzzy sets; Bayesian techniques; Information Gap Decision Theory; Bayesian Information Gap (BIG) Decision Theory;
• optimization (single-/multi-objective, global/local, Levenberg-Marquardt and Particle Swarm methods);
• characterization of flow medium heterogeneity; high-resolution stochastic imaging (tomography); stochastic inversion;
• scale effects; Lévy (alpha-stable) distributions; fractal formalism;
• analytical and numerical simulation methods;
• flow/transport in saturated/unsaturated, porous/fractured media, aquifers, watersheds;
• regional/site scale conceptual and numerical modeling; evaluation of conceptual model uncertainties;
• subsurface fluid dynamics and contaminant transport; well hydraulics; exploration and protection of groundwater resources; design of groundwater-supply systems; capture-zone analyses.

• Mudunuru, M., Ahmmed, B., Rau, E., Vesselinov, V.V., Karra, S., Machine Learning for Geothermal Resource Exploration in Tularosa Basin, New Mexico, Energies, 2023. PDF
• Solander, K.C., Talsma, C.J., Vesselinov, V.V., The drivers and predictability of wildfire re-burns in the western United States (US), Environ. Res.: Climate 2, 2023, 10.1088/2752-5295/acb079 PDF
• Mudunuru, M., Vesselinov, V.V., Ahmmed, B., Geothermalcloud: Machine learning for geothermal resource exploration, Journal of Machine Learning for Modeling and Computing, 10.1615/JMachLearnModelComput.2022046445, 2022. PDF
• Rupe, A., Vesselinov, V.V., Crutchfield, J.P., Nonequilibrium statistical mechanics and optimal prediction of partially-observed complex systems, New Journal of Physics, 10.1088/1367-2630/ac95b7, 2022. PDF
• Vesselinov, V.V., Ahmmed, B., Mudunuru, M., Karra, S., Middleton, R., Discovering hidden geothermal signatures using non-negative matrix factorization with customized k-means clustering, Geothermics, 10.1016/j.geothermics.2022.102576, 2022. PDF
• Ahmmed, B, Vesselinov, V.V., Machine learning and shallow groundwater chemistry to identify geothermal prospects in the Great Basin, Renewable Energy, 10.1016/j.renene.2022.08.024, 2022. PDF
• Vesselinov, V.V., Ahmmed, B., Mudunuru, M., Karra, S., Middleton, R., Discovering the Hidden Geothermal Signatures of Southwest New Mexico, World Geothermal Congress, Reykjavik, Iceland, 2021. PDF
• Wu, H., O'Malley, D., Golden, J.K., Vesselinov, V.V., 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. PDF
• O'Malley, D., Golden, J.K., Vesselinov, V.V., Learning to regularize with a variational autoencoder for hydrologic inverse analysis, arXiv:1906.02401v1, 2021. PDF
• Fleming, S.W., Watson, J.R., Ellenson, A., Cannon, A.J., Vesselinov, V.V., Machine learning in Earth and environmental science requires education and research policy reforms, 10.1038/s41561-021-00865-3, 2021. PDF
• Siler, D.L., Pepin, J.D., Vesselinov, V.V., 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. PDF
• Morra, G., Bozdag, E., Knepley, M., Rass, L., Vesselinov, V.V., A Tectonic Shift in Analytics and Computing Is Coming, AGU EOS, 10.1029/2021EO159258, 2021. 10.1029/2021EO159258, 2021. PDF
• Fleming, S.W., Vesselinov, V.V., Goodbody, A.G., 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. PDF
• Ahmmed, B, Mudunuru, M.K., Karra, S., Vesselinov, V.V., Machine Learning to Discover Mineral Trapping Signatures due to CO2 Injection, International Journal of Greenhouse Gas Control, 10.1016/j.ijggc.2021.103382, 2021. PDF
• Ahmmed, B, Mudunuru, M.K., Karra, S., James, S.C., Vesselinov, V.V., 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. PDF
• Mehana, M., Guiltinan, E., Vesselinov, V.V., Middleton, R., Hyman, J., Kang, Q., Viswanathan, H., Machine-Learning Predictions of the Shale Wells’ Performance, Journal of Natural Gas Science and Engineering, 10.1016/j.jngse.2021.103819, 2021. PDF
• Mudunuru, M.K., Viswanathan, H.S., Carey, J. W., Chen, L., Kang, Q., Karra, S., Vesselinov, V.V., Middleton, R.S., Johnson, P.A., Subsurface energy: Flow and reactive-transport in porous and fractured media, Handbook of Porous Materials (invited), World Scientific Publishers, 10.1142/9789811223419_0004, 2020. PDF
• Lopez, C.A, Vesselinov, V.V., Gnanakaran, S., Alexandrov, B.S., 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. PDF
• Vesselinov, V.V., Mudunuru, M., Karra, S., O'Malley, D., Alexandrov, B.S., 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. PDF
• Alexandrov, B.S., Stanev, V., Vesselinov, V.V., Rasmussen, K., Nonnegative tensor decomposition with custom clustering for microphase separation of block copolymers, Statistical Analysis and Data Mining, 10.1002/sam.11407, 2019. PDF
• Vesselinov, V.V., Alexandrov, B.S., O'Malley, D., Nonnegative Tensor Factorization for Contaminant Source Identification, Journal of Contaminant Hydrology, 10.1016/j.jconhyd.2018.11.010, 2018. PDF
• O'Malley, D., Vesselinov, V.V., Alexandrov, B.S., Alexandrov, L.B., Nonnegative/binary matrix factorization with a D-Wave quantum annealer, PlosOne, 10.1371/journal.pone.0206653, 2018. PDF
• Telfeyan, K., Migdisov, A.A., Pandey, S., Vesselinov, V.V., Reimus, P.W., Long-term stability of dithionite in alkaline anaerobic aqueous solution, Applied Geochemistry, 10.1016/j.apgeochem.2018.12.015, 2018. PDF
• Stanev, V., Vesselinov, V.V., Kusne, A.G., Antoszewski, G., Takeuchi,I., Alexandrov, B.A., 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. PDF
• Iliev, F.L., Stanev, V.G., Vesselinov, V.V., Alexandrov, B.S., Nonnegative Matrix Factorization for identification of unknown number of sources emitting delayed signals PLoS ONE, 10.1371/journal.pone.0193974. 2018. PDF
• Stanev, V.G., Iliev, F.L., Hansen, S.K., Vesselinov, V.V., Alexandrov, B.S., 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. PDF
• Qian, E., Peherstorfer, B., O'Malley, D., Vesselinov, V.V., Wilcox, K., Multifidelity Monte Carlo Estimation of Variance and Sensitivity Indices, SIAM Journal on Uncertainty Quantification, 10.1137/17M1151006, 2018. PDF
• Lu, Z., Vesselinov, V.V., Lei, H., Identifying Arbitrary Parameter Zonation using Multiple Level Set Functions, Journal of Computational Physics, 10.1016/j.jcp.2018.03.016, 2018. PDF
• Hansen, S.K., He, J., Vesselinov, V.V., Characterizing the impact of model error in hydrologic time series recovery inverse problems, 10.1017/j.advwatres.2017.146.R2, Advances in Water Resources, 2018. PDF
• Lin, Y., O'Malley, D., Vesselinov, V.V., Guthrie, G.D, Coblentz, D., Randomization in Characterizing the Subsurface, SIAM News, 2018. PDF
• Hansen, S.K., Haslauer, C.P., Cirpka, O.A., Vesselinov, V.V., Direct Breakthrough Curve Prediction from Statistics of Heterogeneous Conductivity Fields, Water Resources Research, 10.1002/2017WR020450, 2018. PDF
• Vesselinov, V.V., Mudunuru. M., Karra, S., O'Malley, D., Alexandrov, 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. PDF
• Vesselinov, V.V., O'Malley, D., Alexandrov, B.S., Contaminant source identification using semi-supervised machine learning, Journal of Contaminant Hydrology, 10.1016/j.jconhyd.2017.11.002, 2017. PDF
• Hansen, S.K., Pandey, S., Karra, S., Vesselinov, V.V., 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, 10, 4525–4538, 2017. PDF
• Lin, Y, Le, E.B, O'Malley, D., Vesselinov, V.V., Bui-Thanh, T., Large-Scale Inverse Model Analyses Employing Fast Randomized Data Reduction, Water Resources Research, 10.1002/2016WR020299RRR, 2017. PDF
• Hansen, S.K., Vesselinov, V.V., Local equilibrium and retardation revisited, Groundwater, 10.1111/gwat.12551, 2017. PDF
• Hansen, S.K., Vesselinov, V.V., Reimus, P., Lu, Z., Inferring subsurface heterogeneity from push-drift tracer tests, Water Resources Research, 10.1002/2017WR020852R, 2017. PDF
• Bakarji, J., Vesselinov, V.V., O’Malley, D., Agent-based Socio-hydrological Hybrid Modeling for Water Resource Management, Water Resources Management, 10.1007/s11269-017-1713-7, 2017. PDF
• Zhang, X., Sun, A.Y., Duncan, I.J., Vesselinov, V.V., Two-Stage Fracturing Wastewater Management in Shale Gas Development, Ind. Eng. Chem. Res., 10.1021/acs.iecr.6b03971, 2017. PDF
• Zhang, X., Vesselinov, V.V., 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. PDF
• O'Malley, D., Vesselinov, V.V., 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. PDF
• Lin, Y, O'Malley, D., Vesselinov, V.V., A computationally efficient parallel Levenberg-Marquardt algorithm for highly parameterized inverse model analyses, Water Resources Research, 10.1002/2016WR019028, 2016. PDF
• Hansen, S.K., Berkowitz, B., Vesselinov, V.V., O'Malley, D., Karra, S., Push-pull tracer tests: their information content and use for characterizing non-Fickian, mobile-immobile behavior, Water Resources Research, 10.1002/2016WR018769RR, 2016. PDF
• Zhang, X., Vesselinov, V.V., Energy-Water Nexus: Balancing the Tradeoffs between Two-Level Decision Makers Applied Energy, Applied Energy, 10.1016/j.apenergy.2016.08.156, 2016. PDF
• Hansen, S.K., Vesselinov, V.V., Contaminant point source localization error estimates as functions of data quantity and model quality, 10.1016/j.jconhyd.2016.09.003, 2016. PDF
• Throckmorton, H., Newman, B., Heikoop, J., Perkins, G., Feng, X., Graham, D., O'Malley, D., Vesselinov, V.V., Young, J., Wullschleger, S., Wilson, C., Active layer hydrology in an arctic tundra ecosystem: quantifying water sources and cycling using water stable isotopes, Hydrological Processes, 10.1002/hyp.10883, 2016. PDF
• Grasinger, M., O'Malley, D., Vesselinov, V.V., Karra, S., 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. PDF
• Mattis, S.A., Butler, T.D. Dawson, C.N., Estep, D., Vesselinov, V.V., Parameter estimation and prediction for groundwater contamination based on measure theory, Water Resources Research, 10.1002/2015WR017295, 2015. PDF
• O’Malley, D., Vesselinov, V.V., Bayesian-Information-Gap Decision Theory (BIG-DT) with an application to CO2 sequestration, Water Resources Research, 10.1002/2015WR017413, 2015. PDF
• Lu, Z., Vesselinov, V.V., Analytical Sensitivity Analysis of Transient Groundwater Flow in a Bounded Model Domain using Adjoint Method, Water Resources Research, 10.1002/2014WR016819, 2015. PDF
• Barajas-Solano, D. A., Wohlberg, B., Vesselinov, V.V., Tartakovsky, D. M., Linear Functional Minimization for Inverse Modeling, Water Resources Research, 10.1002/2014WR016179, 2015. PDF
• O’Malley, D., Vesselinov, V.V., Cushman, J.H., Diffusive mixing and Tsallis entropy, Physical Review E, 10.1103/PhysRevE.91.042143, 2015. PDF
• O’Malley, D., Vesselinov, V.V., A combined probabilistic/non-probabilistic decision analysis for contaminant remediation, Journal on Uncertainty Quantification, SIAM/ASA, 10.1137/140965132, 2014. PDF
• Vesselinov, V.V., O'Malley, D., Katzman, D., Robust Decision Analysis for Environmental Management of Groundwater Contamination Sites, In Vulnerability, Uncertainty, and Risk Quantification, Mitigation, and Management (ed. Michael Beer, Siu-Kui Au, and Jim W. Hall), 2916 pp, ISBN: 9780784413609, 10.1061/9780784413609.197, 2014. Link
• O’Malley, D., Vesselinov, V.V., Cushman, J.H., A Method for Identifying Diffusive Trajectories with Stochastic Model, Journal of Statistical Physics, Springer, 10.1007/s10955-014-1035-6, 2014. PDF
• Alexandrov, B., Vesselinov, V.V., Blind source separation for groundwater level analysis based on non-negative matrix factorization, Water Resources Research, 10.1002/2013WR015037, 2014. PDF
• O’Malley, D., Vesselinov, V.V., Analytical solutions for anomalous dispersion transport, Advances in Water Resources, 10.1016/j.advwatres.2014.02.006, 2014. PDF
• Heikoop, J.M., Johnson, T.M., Birdsell, K.H., Longmire, P., Hickmott, D.D., Jacobs, E.P., Broxton, D.E., Katzman, D., Vesselinov, V.V., Ding, M., Vaniman, D.T., Reneau, S.L., Goering, T.J., Glessner, J., Basu, A., Isotopic evidence for reduction of anthropogenic hexavalent chromium in Los Alamos National Laboratory groundwater, Chemical Geology, 10.1016/j.chemgeo.2014.02.022, 2014. PDF
• Freedman, V.L., Chen, X., Finsterle, S., Freshley, M., Gorton, I., Gosink, L., Keating, E., Lansing, C., Moeglein W., Murray C., Pau, G., Porter, E., Purohit, S., Rockhold, M., Schuchardt, K., Sivaramakrishnan, C., Vesselinov, V.V., Waichler, S., A high-performance workflow system for subsurface simulation, Environmental Modelling & Software, 55, pp. 176-189, 10.1016/j.envsoft.2014.01.030, 2014. PDF
• O’Malley, D., Vesselinov, V.V., Groundwater remediation using the information gap decision theory, Water Resources Research, 10.1002/2013WR014718, 2014. PDF
• Harp, D.R., Vesselinov, V.V., Accounting for the influence of aquifer heterogeneity on spatial propagation of pumping drawdown, Journal of Water Resource and Hydraulic Engineering, 2(3), pp. 65-83, 2013. PDF
• Vesselinov, V.V., Katzman, D., Broxton, D., Birdsell, K., Reneau, S., Vaniman, D., Longmire, P., Fabryka-Martin, J., Heikoop, J., Ding, M., Hickmott, D., Jacobs, E., Goering, T., Harp, D.R., Mishra, P., Data and Model-Driven Decision Support for Environmental Management of a Chromium Plume at Los Alamos National Laboratory (LANL), Waste Management Symposium 2013, Session 109: ER Challenges: Alternative Approaches for Achieving End State, Phoenix, AZ, http://wmsym.org, 2013. PDF
• Vesselinov, V.V., Pau, G., Finsterle, S, 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), 2012. PDF
• Vesselinov, V.V., Harp, D., Adaptive hybrid optimization strategy for calibration and parameter estimation of physical models, Computers & Geosciences, 10.1016/j.cageo.2012.05.027, 2012. PDF
• Harp, D., Vesselinov, V.V., Contaminant remediation decision analysis using information gap theory, Stochastic Environmental Research and Risk Assessment (SERRA), 10.1007/s00477-012-0573-1, 2012. PDF
• Mishra, P.K., Gupta, H.V., Vesselinov, V.V.; On simulation and analysis of variable-rate pumping tests, Ground Water, 10.1111/j.1745-6584.2012.00961.x, 2012. PDF
• Mishra, P.K., Vesselinov, V.V., Kuhlmna, K.L.; Saturated–unsaturated flow in a compressible leaky-unconfined aquifer, Advances in Water Resources, 10.1016/j.advwatres.2012.03.007, 2012. PDF
• Mishra, P.K., Vesselinov, V.V., Neuman, S.P.; 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. PDF
• Harp, D., Vesselinov, V.V., An agent-based approach to global uncertainty and sensitivity analysis, Computers & Geosciences, 10.1016/j.cageo.2011.06.025, 2012. PDF
• Harp, D., Vesselinov, V.V., 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. PDF
• Mishra, P.K., Vesselinov, V.V., Unified Analytical Solution for Radial Flow to a Well in a Confined Aquifer, arXiv:1110.5940, 2011. PDF
• Vesselinov, V.V., Harp, D., Decision support based on uncertainty quantification of model predictions of contaminant transport, CMWR 2010: XVIII International Conference on Water Resources, J. Carrera (Ed), CIMNE, Barcelona 2010. PDF
• Harp, D., Vesselinov, V.V., Identification of Pumping Influences in Long-Term Water Level Fluctuations, Ground Water, 10.1111/j.1745-6584.2010.00725.x., 2010. PDF
• Morales-Casique, E, Neuman, S.P., Vesselinov, V.V., 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, vol. 24, 10.1007/s00477-010-0383-2, 2010. PDF
• Harp, D., Vesselinov, V.V., Stochastic inverse method for estimation of geostatistical representation of hydrogeologic stratigraphy using borehole logs and pressure, invited, Special issue of Stochastic Environmental Research and Risk Assessment (SERRA) Journal celebrating 70th anniversary of Shlomo P Neuman, vol. 24, 10.1007/s00477-010-0403-2, 2010. PDF
• Vrugt, J., Stauffer, P., Wöhling, Th., Robinson, B., Vesselinov, V.V., Inverse Modeling of Subsurface Flow and Transport Properties Using Recent Advances in Global Optimization, Parallel Computing and Sequential Data Assimilation, Vadose Zone Journal, pp 843-864, 2008. PDF
• Morales-Casique, E., Neuman, S.P., Vesselinov, V.V., Maximum likelihood Bayesian averaging of air flow models in unsaturated fractured tuff, pp.70-75, IAHS Publication 320, ISBN 978-1-901502-49-7, 2008. PDF
• Harp, D., Dai, Z., Wolfsberg, A., Vrugt, J., Robinson, B., Vesselinov, V.V., Aquifer structure identification using stochastic inversion. Geophysical Research Letters L08404, 10.1029/2008GL033585, 2008. PDF
• Vesselinov, V.V., Uncertainties In Transient Capture-Zone Estimates, Computational Methods in Water Resources XVI, (edited by P. Binning, P. Engesgaard, H. Dahle, G. Pinder & W. Gray), Balkema, Rotterdam, ISBN 90-5809-124-4, pp. 307-314, 2006. PDF
• Vrugt, J.A, Robinson, B.A., Vesselinov, V.V., Improved Inverse Modeling in Geophysics: Combined Parameter and State Estimation, Geophysical Research Letters, v.32, L18408, 10.1029/2005GL023940, 2005. PDF
• Vesselinov, V.V., Estimation of parameter uncertainty using inverse model sensitivities, Computational Methods in Water Resources XV (CMWR 2004) (ed. Miller, C., Farthing, M.W., Gray, W.G., Pinder, G.), Elsevier, ISBN 0-444-51839-8, pp. 508-514, 10.1016/S0167-5648(04)80139-4, 2004. PDF

More publications are available at Google Scholar, ResearchGate, and Academia.edu.

• Kliphuis, T.L., Bluehouse, M., Vesselinov, V.V., GeoTGO: Equitable and Inclusive Tool for Community-Based Geothermal, Stanford Geothermal Workshop, Stanford, CA, February 6-8, 2023. PDF
• Vesselinov, V.V., Kliphuis, T.L., Physics-Informed Machine Learning of Geothermal, Geomechanical, Geochemical Process, American Geophysical Union, Fall Meeting, Chicago, IL, December 12-16, 2022. PDF
• Jafarov, E.E., Genet. H., Vesselinov, V.V., Briones, V., Rutter, R., Rogers, B.M., Natali, S., Toward Automated Data-Model Calibration for the Arctic Terrestrial Ecosystem Model, American Geophysical Union, Fall Meeting, Chicago, IL, December 12-16, 2022. PDF
• Vesselinov, V.V., Kliphuis, T.L., Novel machine learning methods and tools for geothermal and geochemical problems, American Geophysical Union, Fall Meeting, Chicago, IL, December 12-16, 2022. PDF
• Fleming, W.W., Vesselinov, V.V., Goodbody, A.G., 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, American Geophysical Union, Fall Meeting, Chicago, IL, December 12-16, 2022. PDF
• Vesselinov, V.V., Kliphuis, T.L., ChemML: Understanding groundwater flow and contaminant transport using machine learning, Frontiers in Hydrology Meeting, American Geophysical Union, San Juan, Puerto Rico, June 19-24, 2022. PDF
• Vesselinov, V.V., et al., Machine Learning to Characterize the State of Stress and its Influence on Geothermal Production, Geothermal Rising Conference, San Diego, CA, October 3-6, 2021.
• Vesselinov, V.V., et al., Hidden geothermal signatures of the southwest New Mexico, World Geothermal Congress, Reykjavik, Iceland, May-Oct, 2021.
• Vesselinov, V.V., et al., SmartTensors: Unsupervised Machine Learning, JuliaCon, Boston, MA, July 28-30, 2021. PDF
  
• Vesselinov, V.V., et al., GeoThermalCloud: Fusion of Big Data and Multi-Physics Models, JuliaCon, Boston, MA, July 28-30, 2021. PDF
• Vesselinov, V.V., et al., ML4Geo: Machine Learning for Geosciences, JuliaCon, Boston, MA, July 28-30, 2021. PDF
• Vesselinov, V.V., et al., GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration and Development of Hidden Geothermal Resources, Department of Energy, Geothermal Office, 2021. PDF
• Vesselinov, V.V., et al., Unsupervised and Physics-Informed Machine learning in Geosciences, Baylor University, Texas, 2021. PDF
• Vesselinov, V.V., et al., Machine learning for geothermal resource analysis and exploration, XXIII International Conference on Computational Methods in Water Resources (CMWR), Stanford, CA, December 13-15, 2020. PDF
• Vesselinov, V.V., Predicting oil and gas production from unconventional tight-rock reservoirs using machine learning, XXIII International Conference on Computational Methods in Water Resources (CMWR), Stanford, CA, December 13-15, 2020. PDF
• Vesselinov, V.V., Unsupervised and Physics-Informed Machine Learning Analyses for Characterization of Energy Production from Unconventional Reservoirs, Machine Learning in Oil & Gas Conference, November 2020. PDF
• Vesselinov, V.V., Unsupervised and Physics-Informed Machine Learning of Big and Noisy Data, Bureau of Economic Geology, University of Austin, Texas, 2020. PDF YouTube
  
• Vesselinov, V.V., et al., Discovering Hidden Geothermal Signatures using Unsupervised Machine Learning, Geothermal Workshop, Stanford, CA, 2020. PDF
• Mudunuru, M.K, Vesselinov, V.V., et al., Site-Scale and Regional-Scale Modeling for Geothermal Resource Analysis and Exploration, Geothermal Workshop, Stanford, CA, 2020. PDF
• Vesselinov, V.V., Machine learning analyses for characterization of oil, gas and water production from unconventional tight-rock reservoirs, AGU Fall Meeting, San Francisco, CA, 2019. PDF
• Vesselinov, V.V., Unsupervised and Physics-Informed Machine Learning of Big Data, Workshop: Applications of Big Data and High-Performance Computing in Earth Sciences, AGU Fall Meeting, San Francisco, CA, 2019, (invited). PDF
• Vesselinov, V.V., Unsupervised Machine Learning: Nonnegative Matrix Tensor Decompositions, MIT, Boston, MA, 2019. PDF
• Vesselinov, V.V., Physics-Informed Machine Learning Methods for Data Analytics and Model Diagnostics, M3 NASA DRIVE Workshop, Los Alamos, 2019. PDF
• Vesselinov, V.V., Machine Learning Analyses of Climate Data and Models, 11th World Congress of European Water Resources Association (EWRA), Madrid, Spain, 2019. PDF
• Vesselinov, V.V., Novel Unsupervised Machine Learning Methods for Data Analytics and Model Diagnostics, Machine Learning in Solid Earth Geoscience, Santa Fe, 2019. PDF
• Vesselinov, V.V., Unsupervised Machine Learning Methods for Feature Extraction, New Mexico Big Data & Analytics Summit, nmbdas.com, Albuquerque, 2019. PDF
• Vesselinov, V.V., Novel Machine Learning Methods for Extraction of Features Characterizing Datasets and Models, AGU Fall meeting, Washington D.C., 2018. PDF
• Vesselinov, V.V., Novel Machine Learning Methods for Extraction of Features Characterizing Complex Datasets and Models, Recent Advances in Machine Learning and Computational Methods for Geoscience, Institute for Mathematics and its Applications, University of Minnesota, 10.13140/RG.2.2.16024.03848, 2018. PDF
  
• Vesselinov, V.V., Mudunuru. M., Karra, S., O'Malley, D., Alexandrov, Unsupervised Machine Learning based on Nonnegative Matrix/Tensor Factorization, World Congress on Computational Mechanics (WCCM), New York, NY, 2018 (invited)
• Vesselinov, V.V., Mudunuru. M., Karra, S., O'Malley, D., Alexandrov, 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. PDF
• O'Malley, D., Vesselinov, V.V., Alexandrov, B.S., Alexandrov, L.B., Nonnegative/binary matrix factorization with a D-Wave quantum annealer PDF
• Vesselinov, V.V., O'Malley, D., Alexandrov, B., Novel Robust Machine Learning Methods for Identification and Extraction of Unknown Features in Complex Real-world Data Sets, Society for Industrial and Applied Mathematics (SIAM) Uncertainty Quantification, Garden Grove, CA, 2018, (invited).
• Vesselinov, V.V., O'Malley, D., Alexandrov, B., Unsupervised Machine Learning Based on Tensor Factorization, International Society for Porous Media (INTERPORE), New Orleans, LA, 10.13140/RG.2.2.15195.00807, 2018. PDF
• Vesselinov, V.V., O'Malley, D., Alexandrov, B., Uncertainty quantification and experimental design based on unsupervised machine learning identification of contaminant sources and groundwater types using hydrogeochemical data, AGU Fall Meeting, New Orleans, LA, 2017 PDF
• O'Malley, D., Vesselinov, V.V., Quo vadis: Hydrologic inverse analyses using high-performance computing and a D-Wave quantum annealer, AGU Fall Meeting, New Orleans, LA, 10.13140/RG.2.2.10161.84320, 2017. PDF
• Lin, Y., Vesselinov, V.V., O'Malley, D., Wohlberg, B., Hydraulic Inverse Modeling using Total-Variation Regularization with Relaxed Variable-Splitting, SIAM Conference on Computational Science and Engineering, Atlanta, GA, 2017. PDF
• Vesselinov, V.V., O'Malley, D., Katzman, D., Decision Analyses for Groundwater Remediation, Waste Management Symposium, Phoenix, AZ, 10.13140/RG.2.2.33273.11367, 2017. PDF
• Vesselinov, V.V., O'Malley, D., Model Analyses of Complex Systems Behavior using MADS, AGU Fall Meeting, San Francisco, CA, 10.13140/RG.2.2.33902.25921, 2016. PDF
• Vesselinov, V.V., O'Malley, D., Alexandrov, B., Moore, B., Reduced Order Models for Decision Analysis and Upscaling of Aquifer Heterogeneity, AGU Fall Meeting, San Francisco, CA, 2016, (invited). PDF
• He, J., Hansen, S.K., Vesselinov, V.V., Analysis of Hydrologic Time Series Reconstruction Uncertainty due to Inverse Model Inadequacy, AGU Fall Meeting, San Francisco, CA, 2016. PDF
• Lu, Z., Vesselinov, V.V., Lei, H., Identifying Aquifer Heterogeneities using the Level Set Method, AGU Fall Meeting, San Francisco, CA, 2016. PDF
• Hansen, S.K., Haslauer, C.P., Cirpka, O.A., Vesselinov, V.V., Prediction of Breakthrough Curves for Conservative and Reactive Transport, AGU Fall Meeting, San Francisco, CA, 2016. PDF
• Zhang, X., Vesselinov, V.V., Bi-Level Decision Making for Supporting Energy and Water Nexus, AGU Fall Meeting, San Francisco, CA, 2016. PDF
• Lin, Y., Vesselinov, V.V., O'Malley, D., Wohlberg, B., Hydraulic Inverse Modeling using Total-Variation Regularization with Relaxed Variable-Splitting, AGU Fall Meeting, San Francisco, CA, 2016. PDF
• Vesselinov, V.V., O'Malley, D., Katzman, D., ZEM: Integrated Framework for Real-Time Data and Model Analyses for Robust Environmental Management Decision Making, Waste Management Symposium, Phoenix, AZ, 10.13140/RG.2.2.29917.67041, 2016. PDF
• Vesselinov, V.V., O'Malley, D., Katzman, D., Model-Assisted Decision Analyses Related to a Chromium Plume at Los Alamos National Laboratory, Waste Management Symposium, Phoenix, AZ, 10.13140/RG.2.2.23206.78404, 2015. PDF
• O'Malley, D., Vesselinov, V.V., Bayesian Information-Gap (BIG) Decision Analysis Applied to a Geologic CO2 Sequestration Problem, AGU Fall Meeting, San Francisco, CA, 2014. PDF
• Cushman, J.H., Vesselinov, V.V., O'Malley, D., Random dispersion coefficients and Tsallis entropy, AGU Fall Meeting, San Francisco, CA, 2014. PDF
• Bakarji, J., O'Malley, D., Vesselinov, V.V., A Social Dynamics Dependent Water Supply Well Contamination Model, LANL Postdoc Research Conference, 10.13140/RG.2.2.17963.90407, 2014. PDF
• Vesselinov, V.V., Alexandrov, B.A, Model-free Source Identification, AGU Fall Meeting, San Francisco, CA, 2014. PDF
• Vesselinov, V.V., Katzman, D., Broxton, D., Birdsell, K., Reneau, S., Vaniman, D., Longmire, P., Fabryka-Martin, J., Heikoop, J., Ding, M., Hickmott, D., Jacobs, E., Goering, T., Harp, D., Mishra, P., Data and Model-Driven Decision Support for Environmental Management of a Chromium Plume at Los Alamos National Laboratory (LANL), Session 109: Environmental Restoration Challenges: Alternative Approaches for Achieving End State, Waste Management Symposium, Phoenix, AZ, February 28, 10.13140/RG.2.2.10414.15686, 2013. PDF
• Vesselinov, V.V., Harp, D., Katzman, D., Model-driven decision support for monitoring network design based on analysis of data and model uncertainties: methods and applications, H32F: Uncertainty Quantification and Parameter Estimation: Impacts on Risk and Decision Making, AGU Fall meeting, San Francisco, December 3-7, 10.13140/RG.2.2.17125.04321, LA-UR-13-20189, 2012, (invited). PDF
• Vesselinov, V.V., et al., AGNI: Coupling Model Analysis Tools and High-Performance Subsurface Flow and Transport Simulators for Risk and Performance Assessments, XIX International Conference on Computational Methods in Water Resources (CMWR 2012), University of Illinois at Urbana-Champaign, June 17-22, 2012. PDF
• Leif Zinn-Bjorkman, L., Numerical Optimization using the Levenberg-Marquardt Algorithm, EES-16 Seminar Series, LA-UR-11-12010, 10.13140/RG.2.2.11253.01760, 2011. PDF
• Harp, D., Vesselinov, V.V., Recent developments in MADS algorithms: ABAGUS and Squads, EES-16 Seminar Series, LA-UR-11-11957, 10.13140/RG.2.2.35579.98082, 2011. PDF
• Vesselinov, V.V., et al., Environmental Management Modeling Activities at Los Alamos National Laboratory (LANL), Department of Energy Technical Exchange Meeting, Performance Assessment Community of Practice, Hanford, April 13-14, 10.13140/RG.2.2.12091.87846, 2010. PDF
• Vesselinov, V.V., Harp, D., Koch, R., Birdsell, K., Katzman, K., Tomographic inverse estimation of aquifer properties based on pressure variations caused by transient water-supply pumping, AGU Meeting, San Francisco, CA, December 15-19, 10.13140/RG.2.2.25513.65126, 2008. PDF
• Vesselinov, V.V., Uncertainties in Transient Capture-Zone Estimates, CMWR 2006 XVI International Conference on Computational Methods in Water Resources, Copenhagen, Denmark, June 18-22, 10.13140/RG.2.2.28869.09446, 2006. PDF

More presentations are available at SlideShare.net, ResearchGate, and Academia.edu.

• Vesselinov, V.V., et al., SmartTensors: Unsupervised Machine Learning, JuliaCon, Boston, MA, July 28-30, 2021. PDF YouTube
  
• SmartTensors for Unsupervised and Physics-Informed Machine Learning and Artificial Intelligence (ML/AI): a R&D100 award promotional video, 2021. YouTube
  
• Vesselinov, V.V., Unsupervised and Physics-Informed Machine Learning of Big and Noisy Data, Bureau of Economic Geology, University of Austin, Texas, 2020. YouTube
  
• Vesselinov, V.V., Machine Learning Deconstruction of the Oklahoma seismic events caused by oil/gas production activities, 2019. YouTube
  
• Vesselinov, V.V., Machine Learning for characterization of geothermal activities causing seismic effects at the Geysers geothermal field, 2019. YouTube
  
• Vesselinov, V.V., Machine Learning of the Phase separation of co-polymers, 2019. YouTube
  
• Vesselinov, V.V., Furusho-Percot, C., Goergen, K., Kollet, S., Machine Learning deconstruction of European air temperature fluctuations in 2003 using Terrestrial Systems Modeling Platform (TSMP) model outputs, 2018. YouTube
  
• Vesselinov, V.V., O'Malley, D., Machine Learning for charecterization of the LANL Chromium plume in the regional aquifer, 2018. YouTube
  
• Vesselinov, V.V., Novel Machine Learning Methods for Extraction of Features Characterizing Complex Datasets and Models, Recent Advances in Machine Learning and Computational Methods for Geoscience, Institute for Mathematics and its Applications, University of Minnesota, 2018. YouTube
  

More videos are available on my ML YouTube channel.

• Fate and Transport Investigations Update for Chromium Contamination from Sandia Canyon, LA-UR-08-4702, 2008. PDF
• Pajarito Canyon Investigation Report, LA-UR-08-5852, 2008. PDF
• Decision analysis for addressing groundwater contaminants from the radioactive liquid waste treatment facility released into Mortandad canyon, LA-UR-05-6397, 2005. PDF

More reports are available at the LANL electronic public reading room

• Ahmmed, B, Vesselinov, V.V., Machine Learning for Small, Uncertain, and Sparse Data Sets, American Geophysical Union, Fall Meeting, Chicago, IL, December 12-16, 2022.
• Ahmmed, B, Vesselinov, V.V., Unspervised Machine Learning, American Geophysical Union, Fall Meeting, December 1-17, 2021.
• Ahmmed, B, Vesselinov, V.V., Unspervised Machine Learning in Geosciences, Geological Society of America Annual Meeting, Portland, OR, October 10-13, 2021.

• Nair, R., Zaki, K.S., Li, Y., Rijken, P., Vesselinov, V.V., Geomechanics Informed Machine Intelligence (GIMI)
• Pan, Y., Chang, O., Manley, S., Vesselinov, V.V., Machine Learning for Tight Rock Unconventional Well Production Forecast with Uncertainty Quantification
• Alexandrov, B.S., Vesselinov, V.V., Alexandrov, L.B., Stanev, V., Iliev, F.L., Source identification by non-negative matrix factorization combined with semi-supervised clustering US20180060758A1

In addition to my scientific pursuits, I have always been interested in arts.

My artwork includes drawings, photography, and acrylic paintings.

• Self-portrait
  
  
  
• In the Woods
  
  
  
• Departure for an Adventure: Mykonos, Greece
  
  
  
• Alps Sunset: Schneefernerhaus, Garmisch-Partenkirchen, Germany
  
  
  

Unsupervised Machine Learning (ML) methods are powerful data-analytics tools capable of extracting important features hidden (latent) in large datasets without any prior information. The physical interpretation of the extracted features is done a posteriori by subject-matter experts.

In contrast, supervised ML methods are trained based on large labeled datasets The labeling is performed a priori by subject-matter experts. The process of deep ML commonly includes both unsupervised and supervised techniques LeCun, Bengio, and Hinton 2015 where unsupervised Machine Learning are applied to facilitate the process of data labeling.

The integration of large datasets, powerful computational capabilities, and affordable data storage has resulted in the widespread use of ML/AI in science, technology, and industry.

Recently, we have developed a novel unsupervised and physics-informed ML methods. The methods utilize Matrix/Tensor Decomposition (Factorization) coupled with physics, sparsity and nonnegativity constraints. The methods are capable to reveal the temporal and spatial footprints of the extracted features.

SmartTensors

SmartTensors is a general framework for Unsupervised and Physics-Informed Machine Learning and Artificial Intelligence (ML/AI).

SmartTensors incorporates a novel unsupervised ML based on tensor decomposition coupled with physics, sparsity and nonnegativity constraints.

SmartTensors has been applied to extract the temporal and spatial footprints of the features in multi-dimensional datasets in the form of multi-way arrays or tensors.

The decomposition (factorization) of a given tensor $X$ is typically performed by minimization of the Frobenius norm:

$$\frac{1}{2} ||X-G \otimes_1 A_1 \otimes_2 A_2 \dots \otimes_n A_n ||_F^2$$

where:

• $n$ is the dimensionality of the tensor $X$
• $G$ is a "mixing" core tensor
• $A_1,A_2,\dots,A_n$ are "feature” factors (in the form of vectors or matrices)
• $\otimes$ is a tensor product applied to fold-in factors $A_1,A_2,\dots,A_n$ in each of the tensor dimensions

The product $G \otimes_1 A_1 \otimes_2 A_2 \dots \otimes_n A_n$ is an estimate of $X$ ($X_{est}$).

The reconstruction error $X - X_{est}$ is expected to be random uncorrelated noise.

$G$ is a $n$-dimensional tensor with a size and a rank lower than the size and the rank of $X$. The size of tensor $G$ defines the number of extracted features (signals) in each of the tensor dimensions.

The factor matrices $A_1,A_2,\dots,A_n$ represent the extracted features (signals) in each of the tensor dimensions. The number of matrix columns equals the number of features in the respective tensor dimensions (if there is only 1 column, the particular factor is a vector). The number of matrix rows in each factor (matrix) $A_i$ equals the size of tensor X in the respective dimensions.

The elements of tensor $G$ define how the features along each dimension ($A_1,A_2,\dots,A_n$) are mixed to represent the original tensor $X$.

The tensor decomposition is commonly performed using Candecomp/Parafac (CP) or Tucker decomposition models.

Some of the decomposition models can theoretically lead to unique solutions under specific, albeit rarely satisfied, noiseless conditions. When these conditions are not satisfied, additional minimization constraints can assist the factorization.

A popular approach is to add sparsity and nonnegative constraints. Sparsity constraints on the elements of G reduce the number of features and their mixing (by having as many zero entries as possible). Nonnegativity enforces parts-based representation of the original data which also allows the tensor decomposition results for $G$ and $A_1,A_2,\dots,A_n$ to be easily interrelated Cichocki et al, 2009.

SmartTensors algorithms called NMFk and NTFk for Matrix/Tensor Factorization (Decomposition) coupled with sparsity and nonnegativity constraints custom k-means clustering has been developed in Julia

SmartTensors codes are available as open source on GitHub.

Research Papers:

• Vesselinov, V.V., Mudunuru, M., Karra, S., O'Malley, D., Alexandrov, B.S., Unsupervised Machine Learning Based on Non-Negative Tensor Factorization for Analyzing Reactive-Mixing, 10.1016/j.jcp.2019.05.039, Journal of Computational Physics, 2019. PDF
• Vesselinov, V.V., Alexandrov, B.S., O'Malley, D., Nonnegative Tensor Factorization for Contaminant Source Identification, Journal of Contaminant Hydrology, 10.1016/j.jconhyd.2018.11.010, 2018. PDF
• O'Malley, D., Vesselinov, V.V., Alexandrov, B.S., Alexandrov, L.B., Nonnegative/binary matrix factorization with a D-Wave quantum annealer, PlosOne, 10.1371/journal.pone.0206653, 2018. PDF
• Stanev, V., Vesselinov, V.V., Kusne, A.G., Antoszewski, G., Takeuchi,I., Alexandrov, B.A., 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. PDF
• Iliev, F.L., Stanev, V.G., Vesselinov, V.V., Alexandrov, B.S., Nonnegative Matrix Factorization for identification of unknown number of sources emitting delayed signals PLoS ONE, 10.1371/journal.pone.0193974. 2018. PDF
• Stanev, V.G., Iliev, F.L., Hansen, S.K., Vesselinov, V.V., Alexandrov, B.S., 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. PDF
• Vesselinov, V.V., O'Malley, D., Alexandrov, B.S., Contaminant source identification using semi-supervised machine learning, Journal of Contaminant Hydrology, 10.1016/j.jconhyd.2017.11.002, 2017. PDF
• Alexandrov, B., Vesselinov, V.V., Blind source separation for groundwater level analysis based on non-negative matrix factorization, Water Resources Research, 10.1002/2013WR015037, 2014. PDF

Presentations:

• Vesselinov, V.V., Novel Machine Learning Methods for Extraction of Features Characterizing Datasets and Models, AGU Fall meeting, Washington D.C., 2018. PDF
• Vesselinov, V.V., Novel Machine Learning Methods for Extraction of Features Characterizing Complex Datasets and Models, Recent Advances in Machine Learning and Computational Methods for Geoscience, Institute for Mathematics and its Applications, University of Minnesota, 2018. PDF
• O'Malley, D., Vesselinov, V.V., Alexandrov, B.S., Alexandrov, L.B., Nonnegative/binary matrix factorization with a D-Wave quantum annealer PDF
• Vesselinov, V.V., Alexandrov, B.A, Model-free Source Identification, AGU Fall Meeting, San Francisco, CA, 2014. PDF

Presentations are also available at slideshare.net

Videos:

• Vesselinov, V.V., Unsupervised and Physics-Informed Machine Learning of Big and Noisy Data, Bureau of Economic Geology, University of Austin, Texas, 2020.
• Vesselinov, V.V., Novel Machine Learning Methods for Extraction of Features Characterizing Complex Datasets and Models, Recent Advances in Machine Learning and Computational Methods for Geoscience, Institute for Mathematics and its Applications, University of Minnesota, 2018.

Examples:

• Blind Source Separation (i.e. Feature Extraction)
• Source Identification

Data analytics work executed under most of the projects and practical applications has been performed using the wide range of novel theoretical methods and computational tools developed over the years.

Key tools for data analytics include:

• ZEM: Integrated Framework for Real-Time Data and Model Analyses for Robust Environmental Management Decision Making.
• MADS (Model Analysis & Decision Support): a framework with a wide range of data pre- and post-processing capabilities (including visualization and statistical analyses). MADS can also perform various of data-based analyses.
• CHiPBETA: Correcting pressure head for pumping, barometric, & Earth Tide effects for data-analytics and model-diagnostics applications.
• Kriging.jl: Gaussian process regressions and simulations.

Model diagnostics work executed under most of the projects and practical applications has been performed using the wide range of novel theoretical methods and computational tools developed over the years.

A key tool for model diagnostics is the MADS (Model Analysis & Decision Support) framework. MADS is an integrated open-source high-performance computational (HPC) framework.

MADS can execute a wide range of data- and model-based analyses:

• Sensitivity Analysis
• Parameter Estimation (PE), Model Inversion and Calibration
• Uncertainty Quantification (UQ)
• Model Selection and Model Averaging
• Model Reduction and Surrogate Modeling
• Machine Learning and Blind Source Separation
• Decision Analysis and Support

MADS has been tested to perform HPC simulations on a wide-range multi-processor clusters and parallel environments (Moab, Slurm, etc.).

MADS utilizes adaptive rules and techniques which allows the analyses to be performed with a minimum user input.

MADS provides a series of alternative algorithms to execute each type of data- and model-based analyses.

MADS can be externally coupled with any existing simulator through integrated modules that generate input files required by the simulator and parse output files generated by the simulator using a set of template and instruction files.

MADS also provides internally coupling with a series of built-in analytical simulators of groundwater flow and contaminant transport in aquifers.

MADS has been successfully applied to perform various model analyses related to environmental management of contamination sites. Examples include solutions of source identification problems, quantification of uncertainty, model calibration, and optimization of monitoring networks.

MADS current stable version has been actively updated.

Professional softwares/codes with somewhat similar but not equivalent capabilities are:

• DAKOTA
• UCODE
• PEST
• AGNI / ASCEM (under development)

MADS source code and example input/output files are available at the MADS website.

MADS documentation is available at github and gilab.

The C version of the MADS code is also available: MADS C website and MADS C source . A Python interface for MADS is under development: Python

Other key tools for model diagnostics include:

• AffineInvariantMCMC.jl: Integrated Framework for Real-Time Data and Model Analyses for Robust Environmental Management Decision Making.
• BIGUQ.jl: Bayesian Information Gap Decision Theory (BIG-DT) analysis for Uncertainty Quantification, Experimental Design and Decision Analysis.

SmartTensors is a general framework for Unsupervised and Physics-Informed Machine Learning (ML) using Nonnegative Matrix/Tensor decomposition algorithms.

NMFk/NTFk (Nonnegative Matrix Factorization/Nonnegative Tensor Factorization) are two of the codes within the SmartTensors perform.

Unsupervised ML methods can be applied for feature extraction, blind source separation, model diagnostics, detection of disruptions and anomalies, image recognition, discovery of unknown dependencies and phenomena represented in datasets as well as development of physics and reduced-order models representing the data. A series of novel unsupervised ML methods based on matrix and tensor factorizations, called NMFk and NTFk have been developed allowing for objective, unbiased, data analyses to extract essential features hidden in data. The methodology is capable of identifying the unknown number of features charactering the analyzed datasets, as well as the spatial footprints and temporal signatures of the features in the explored domain.

SmartTensors algorithms are written in Julia.

SmartTensors codes are available as open-source on GitHub

SmartTensors can utilize various external compuiting platforms, including Flux.jl, TensorFlow, PyTorch, MXNet, and MatLab

SmartTensors is currently funded by DOE for commercial deployment (with JuliaComputing) through the Technology Commercialization Fund (TCF).

MADS (Model Analysis & Decision Support) is an integrated open-source high-performance computational (HPC) framework.

MADS can execute a wide range of data- and model-based analyses:

• Sensitivity Analysis
• Parameter Estimation (PE), Model Inversion and Calibration
• Uncertainty Quantification (UQ)
• Model Selection and Model Averaging
• Model Reduction and Surrogate Modeling
• Machine Learning and Blind Source Separation
• Decision Analysis and Support

MADS has been tested to perform HPC simulations on a wide-range multi-processor clusters and cloud parallel environments (Moab, Slurm, etc.).

MADS utilizes adaptive rules and techniques which allows the analyses to be performed with a minimum user input.

MADS provides a series of alternative algorithms to execute each type of data- and model-based analyses.

MADS can be externally coupled with any existing simulator through integrated modules that generate input files required by the simulator and parse output files generated by the simulator using a set of template and instruction files.

MADS also provides internally coupling with a series of built-in analytical simulators of groundwater flow and contaminant transport in aquifers.

MADS has been successfully applied to perform various model analyses related to environmental management of contamination sites. Examples include solutions of source identification problems, quantification of uncertainty, model calibration, and optimization of monitoring networks.

MADS current stable version has been actively updated.

Codes with somewhat similar but not equivalent capabilities are:

• DAKOTA
• UCODE
• PEST
• AGNI / ASCEM (under development)

MADS source code and example input/output files are available at the MADS website.

MADS documentation is available at github and gilab.

MADS old sites: LANL, LANL C, LANL Julia, LANL Python

WELLS

WELLS is a code simulating drawdowns caused by multiple pumping/injecting wells using analytical solutions. WELLS has a C and Julia language versions.

WELLS can represent pumping in confined, unconfined, and leaky aquifers.

WELLS applies the principle of superposition to account for transients in the pumping regime and multiple sources (pumping wells).

WELLS can apply a temporal trend of water-level change to account for non-pumping influences (e.g. recharge trend).

WELLS can account early time behavior by using exponential functions (transmissivities and storativities; Harp and Vesselinov, 2013).

WELLS analytical solutions include:

• confined aquifer (Theis, Mishra et al)
• unconfined aquifer (transformed Theis, Mishra & Neuman)
• leaky confined aquifer (Hantish, Mishra et al)
• leaky unconfined aquifer (Mishra et al)
• fully and partially penetrating pumping well(s)
• fully and partially penetrating observation well(s)
• transient pumping rates: step changes and linear changes (Mishra et al)

WELLS has been applied to decompose transient water-supply pumping influences in observed water levels at the LANL site (Harp and Vesselinov, 2010a).

For example, the figure below shows WELLS simulated drawdowns caused by pumping of PM-2, PM-3, PM-4 and PM-5 on water levels observed at R-15.

The mode inversion of the WELLS model predictions is achieved using the code MADS.

Codes with similar capabilities are AquiferTest. AquiferWin32, Aqtesolv, MLU, and WTAQ.

WELLS source code, example input/output files, and a manual are available at the WELLS websites: LANL GitLab Julia GitHub

LA-CC-10-019, LA-CC-11-098

MPEST

MPEST is a parallel version of the code PEST (Doherty 2009).

MPEST has been developed to optimize the solving of parallel optimization problems using distributed computing.

MPEST has been applied in many parallel computing projects worldwide.

MPEST parallel subroutines has been imported and further developed in the code MADS.

The source code, example input/output files, and a manual are available at MADS website.

ML4Geo: Machine Learning based Well Design to Enhance Unconventional Energy Production

Project PI: Velimir V Vesselinov

ML4Geo is a project funded by the U.S. Department of Energy ARPA E.

ML4Geo includes collaborators from MIT, Stanford, University of Texas-Austin, JuliaComputing, Descartes Lab, and Chevron.

GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

Project PI: Velimir V Vesselinov

GeoThermalCloud is a project funded by the Geothermal Office of the U.S. Department of Energy EERE (Energy Efficiency and Renewable Energy).

GeoThermalCloud includes collaborators from Google, Descartes Lab, Stanford, and University of Texas-Austin.

The DOE Carbon Storage Assurance Facility Enterprise (CarbonSAFE) initiative focuses on development of geologic storage sites for the storage of 50+ million metric tons (MMT) of carbon dioxide (CO2) from existing industrial sources. There are several ongoing CarbonSAFE projects. They aim to improve understanding of site screening, site selection, characterization, baseline monitoring, verification, accounting (MVA), and assessment procedures related to safe carbon storage and sequestration. They also target to develop the information necessary to develop appropriate permits and to design injection and monitoring strategies for commercial-scale projects. The CarbonSAFE efforts will contribute to the development of 50+ MMT storage sites.

I have been the Machine Learning PI of the CarbonSAFE project led by University of Wyoming.

DiaMonD: Mathematics at the Interfaces of Data, Models, and Decisions

LANL PI: Velimir V Vesselinov

DiaMonD is a project funded by the U.S. Department of Energy Office of Science.

DiaMonD addresses Mathematics at the Interfaces of Data, Models, and Decisions.

DiaMonD involves researchers from Colorado State University, Florida State University, Los Alamos National Laboratory, Massachusetts Institute of Technology, Oak Ridge National Laboratory, University of Texas at Austin, and Stanford University.

More information about the DiaMonD work, collaborators, and publications can be find on the project web site.

A project developed computer code:

• MADS: Model Analysis & Decision Support

ASCEM

Advanced Simulation Capability for Environmental Management

Decision Support PI: Velimir V Vesselinov

A consortium of multiple national laboratories has developed high-performance computing capabilities to meet the challenge of waste disposal and cleanup left over from the creation of the US nuclear stockpile decades ago. The project is funded by the Department of Energy Office for Environmental Management (DOE-EM).

Within ASCEM, the goal of the "Decision Support" task is to create a computational framework that facilitates the decision making by site-application users, modelers, stakeholders, and decision/policy makers. The decision-support framework leverages on existing and novel theoretical methods and computational techniques to meet the general decision-making needs of DOE-EM as well as the particular site-specific needs of individual environmental management sites.

The decision-support framework can be applied to identify what kind of model analyses should be performed to mitigate the risk at a given environmental management site, and, if needed, support the design of data-acquisition campaigns, field experiments, monitoring networks, and remedial systems. Depending on the problem, decision-support framework utilizes various types of model analyses such as parameter estimation, sensitivity analysis, uncertainty quantification, risk assessment, experimental design, cost estimation, data-worth (value of information) analysis, etc.

Relevant computer codes:

• MADS: Model Analysis & Decision Support
• Amanzi: Model Simulator

LANL Environmental Management

PI of Vadose Zone and Regional Aquifer Flow and Transport Modeling: Velimir V Vesselinov

Los Alamos National Laboratory (LANL) is a complex site for environmental management. The site encompasses about 100 km2 (37 square miles) of terrain with 600 m (2,000 feet) of elevation change, and an average rainfall of less than 300-400 mm (12 to 16 inches) per year. The site is intersected by 14 major canyon systems. Ecosystems within the site range from riparian to high desert and boast over 2,000 archaeological sites, as well as endangered species habitats.  The surface and subsurface water flow discharges primarily along the Rio Grande to the east of LANL. The Rio Grande traverses the Española basin from north to south; several major municipalities use the river water downgradient from LANL for water supply (Santa Fe, Albuquerque, El Paso/Juarez).

Regional aquifer beneath LANL

The regional aquifer beneath LANL is a complex hydrogeological system. The regional aquifer extends throughout the Española basin, and is an important source for municipal water supply for Santa Fe, Los Alamos, Española, LANL, and several Native-American Pueblos. The wells providing groundwater from this aquifer for Los Alamos and LANL are located within the LANL site and in close proximity to existing contamination sites. The regional aquifer is comprised of sediments and lavas with heterogeneous flow and transport properties. The general shape of the regional water table is predominantly controlled by the areas of regional recharge to the west (the flanks of the Sierra de los Valles and the Pajarito fault zone) and discharge to the east (the Rio Grande and the White Rock Canyon Springs). At more local scales, the structure of groundwater flow is also influenced by (1) local infiltration zones (e.g., beneath wet canyons); (2) heterogeneity and anisotropy in the aquifer properties; and (3) discharge zones (municipal water-supply wells and springs). The aquifer is also characterized by well-defined, vertical stratification, which, in general, provides sufficient protection of the deep groundwater resources.

The vadose zone, between the ground surface and the top of the regional aquifer, is about 180-300 m (600-1000 ft) thick. The vadose zone is comprised of sediments and lavas with heterogeneous flow and transport properties. The variably-saturated flow and transport through the thick vadose zone occurs through pores and fractures, and is predominantly vertical with lateral deviations along perching zones. The groundwater velocities in the vadose zone are high beneath wet canyons (up to 1 m/a) and low beneath the mesas (1 mm/a). Due to complexities in local hydrogeologic conditions, the hydraulic separation between the regional aquifer and the vadose zone is difficult to identify at some localities, especially where mountain-front recharge is pronounced.

The complexity and size of the LANL site make environmental management a continuing engineering and scientific challenge. Legacy contamination—both chemical and radioactive—exists at many locations. Some of the oldest worldwide radioactive Material Disposal Areas (MDA’s), where waste is buried in pits and shafts, are located on the site. LANL is mandated to follow timetables and requirements specified by the Compliance Order on Consent from the New Mexico Environment Department (NMED) for investigation, monitoring, and remediation of hazardous constituents and contaminated sites.

The environmental work performed at the LANL site is managed by the Environmental Programs (EP) Directorate. A team of external and LANL (Computational Earth Sciences Group, Earth & Environmental Sciences) researchers is tasked by the EP Directorate to provide modeling and decision support to enable scientifically-defensible mitigation of the risks associated with various LANL sites. The principal investigator of this team for more than a decade has been Velimir Vesselinov.

Since the 1950's, the LANL site has been the subject of intensive studies for characterization of the site conditions, including regional geology and hydrogeology. Various types of research have been performed at the site related to contaminant transport in the environment which include (1) laboratory experiments, (2) field tests, and (3) conceptual and numerical model analyses. The work is presented in a series of technical reports and peer-reviewed publications.

Important aspects of the environmental management at the LANL site include:

• design of a long-term monitoring network of groundwater flow and transport in the vadose zone and regional aquifer;
• investigation of the hexavalent chromium plume in the regional aquifer; and
• model-based analyses of the environmental impact caused by Material Disposal Areas (MDA’s): performance assessment (PA) and corrective measures evaluations (CME).

Chromium plume in the regional aquifer

A chromium plume has been identified in the regional aquifer beneath the LANL site. Our team has been tasked with providing modeling decision support to the Environmental Programs (EP) Directorate to enable scientifically-defensible mitigation of the risks associated with chromium migration in the environment. A large amount of data and information are available related to the chromium site (vadose-zone moisture content, aquifer water levels, contaminant concentrations, geologic observations, drilling logs, etc.); they are used to develop and refine conceptual and numerical models of the contaminant transport in the environment. The development of numerical models and performance of model analyses (model calibration, sensitivity analyses, parameter estimations, uncertainty quantification, source identification, data-worth analyses, monitoring-network design, etc.) is a computationally intensive effort due to large model domains, large numbers of computational nodes, complex flow media (porous and fracture flow), and long model-execution times. Due to complexities in the model-parameter space, most of the model analyses require a substantial number of model executions. To improve computational effectiveness, our team utilizes state-of-the-art parallel computational resources and novel theoretical and computational methods for model calibration, uncertainty analysis, risk assessment, and decision support.

Numerical modeling of flow and transport in the regional aquifer near the Sandia Canyon

The numerical model is capturing current conceptual understanding and calibrated against existing data (taking into account uncertainties)

Regardless of existing uncertainties, the model provide information related to:

• spatial distribution of contaminant mass,
• contaminant flux to the regional aquifer,
• monitoring-network design, and
• environmental risk.

Relevant computer codes:

• MADS: Model Analysis & Decision Support
• WELLS: Analytical simulator of drawdowns caused by multiple pumping wells
• ChroTran: Contaminant simulator (GitHub, Source)