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Unsupervised machine learning based on non-negative tensor factorization for analyzing reactive-mixing.

V. Vesselinov, M. Mudunuru, S. Karra, D. O'Malley, and B. Alexandrov. J. Comput. Physics, (2019)

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dfnWorks: A discrete fracture network framework for modeling subsurface flow and transport.J. Hyman, S. Karra, N. Makedonska, C. Gable, S. Painter, and H. Viswanathan. Computers & Geosciences, (2015)PFLOTRAN-SIP: A PFLOTRAN Module for Simulating Spectral-Induced Polarization of Electrical Impedance Data.B. Ahmmed, M. Mudunuru, S. Karra, S. James, H. Viswanathan, and J. Dunbar. CoRR, (2019)Joint geophysical and flow inversion to characterize fracture networks in subsurface systems.M. Mudunuru, S. Karra, N. Makedonska, and T. Chen. CoRR, (2016)Sequential geophysical and flow inversion to characterize fracture networks in subsurface systems.M. Mudunuru, S. Karra, N. Makedonska, and T. Chen. Statistical Analysis and Data Mining, 10 (5): 326-342 (2017)Assessing dual continuum method for multicomponent reactive transport.A. Iraola, P. Trinchero, S. Karra, and J. Molinero. Computers & Geosciences, (2019)The FluidFlower Validation Benchmark Study for the Storage of CO2B. Flemisch, J. Nordbotten, M. Fernø, R. Juanes, J. Both, H. Class, M. Delshad, F. Doster, J. Ennis-King, J. Franc and 24 other author(s). Transport in Porous Media, (2023)Unsupervised machine learning based on non-negative tensor factorization for analyzing reactive-mixing.V. Vesselinov, M. Mudunuru, S. Karra, D. O'Malley, and B. Alexandrov. J. Comput. Physics, (2019)Estimating Failure in Brittle Materials using Graph Theory.M. Mudunuru, N. Panda, S. Karra, G. Srinivasan, V. Chau, E. Rougier, A. Hunter, and H. Viswanathan. CoRR, (2018)Towards Solving the Navier-Stokes Equation on Quantum Computers.N. Ray, T. Banerjee, B. Nadiga, and S. Karra. CoRR, (2019)On Maxwell fluid with relaxation time and viscosity depending on the pressureS. Karra, V. Prusa, and K. Rajagopal. CoRR, (2010)

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Unsupervised machine learning based on non-negative tensor factorization for analyzing reactive-mixing.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Unsupervised machine learning based on non-negative tensor factorization for analyzing reactive-mixing.

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Unsupervised machine learning based on non-negative tensor factorization for analyzing reactive-mixing.