mads.lanl.govMADS: Model Analysis & Decision Support

MADS: Model Analysis & Decision Support Documentation Research Downloads Contact menu MADS mads@lanl.gov Documentation Research Downloads Contact MADS Model Analysis & Decision Support open-source high-performance computational framework for data- & model-based analyses in Julia and C MADS can perform: Sensitivity Analysis Parameter Estimation Model Inversion and Calibration Uncertainty Quantification Model Selection and Model Averaging Model Reduction and Surrogate Modeling Machine Learning and Blind Source Separation Decision Analysis and Support MADS can be internally or externally coupled with any existing model simulator. MADS includes built-in analytical solutions for groundwater flow and contaminant transport. MADS includes built-in test functions. MADS includes verification and example problems. MADS performs automatic bookkeeping of model results for efficient restarts and reruns. MADS has been successfully applied to perform analyses related to environmental management. MADS is released in two compatible versions: MADS v1.0 written in Julia (actively developed) MADS v1.1.14 written in C/C++ (continued support) Documentation & Examples Julia version C version search closeTheory & Research Publications 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, 2018 (in review). 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 Greens 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., Gutrie, 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., 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...