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Maruti Kumar Mudunuru
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Unsupervised machine learning based on non-negative tensor factorization for analyzing reactive-mixing
VV Vesselinov, MK Mudunuru, S Karra, D O'Malley, BS Alexandrov
Journal of Computational Physics 395, 85-104, 2019
372019
Reduced-order modeling through machine learning and graph-theoretic approaches for brittle fracture applications
A Hunter, BA Moore, M Mudunuru, V Chau, R Tchoua, C Nyshadham, ...
Computational Materials Science 157, 87-98, 2019
37*2019
A numerical framework for diffusion-controlled bimolecular-reactive systems to enforce maximum principles and the non-negative constraint
KB Nakshatrala, MK Mudunuru, AJ Valocchi
Journal of Computational Physics 253, 278-307, 2013
352013
On enforcing maximum principles and achieving element-wise species balance for advection–diffusion–reaction equations under the finite element method
MK Mudunuru, KB Nakshatrala
Journal of Computational Physics 305, 448-493, 2016
312016
Regression-based reduced-order models to predict transient thermal output for enhanced geothermal systems
MK Mudunuru, S Karra, DR Harp, GD Guthrie, HS Viswanathan
Geothermics 70, 192-205, 2017
252017
Material degradation due to moisture and temperature. Part 1: Mathematical model, analysis, and analytical solutions
C Xu, MK Mudunuru, KB Nakshatrala
Continuum Mechanics and Thermodynamics 28 (6), 1847-1885, 2016
242016
A framework for coupled deformation–diffusion analysis with application to degradation/healing
MK Mudunuru, KB Nakshatrala
International Journal for Numerical Methods in Engineering 89 (9), 1144-1170, 2012
232012
Sequential geophysical and flow inversion to characterize fracture networks in subsurface systems
MK Mudunuru, S Karra, N Makedonska, T Chen
Statistical Analysis and Data Mining: The ASA Data Science Journal 10 (5 …, 2017
202017
Machine learning to identify geologic factors associated with production in geothermal fields: a case-study using 3D geologic data, Brady geothermal field, Nevada
DL Siler, JD Pepin, VV Vesselinov, MK Mudunuru, B Ahmmed
Geothermal Energy 9 (1), 1-17, 2021
162021
Physics-informed Machine Learning for Real-time Unconventional Reservoir Management
MK Mudunuru, D O’Malley, S Srinivasan, JD Hyman, MR Sweeney, ...
AAAI 2020 Spring Symposium on Combining Artificial Intelligence and …, 2020
16*2020
On mesh restrictions to satisfy comparison principles, maximum principles, and the non-negative constraint: Recent developments and new results
M Mudunuru, KB Nakshatrala
Mechanics of Advanced Materials and Structures 24 (7), 556-590, 2017
162017
Discovering signatures of hidden geothermal resources based on unsupervised learning
VV Vesselinov, MK Mudunuru, B Ahmmed, S Karra, RS Middleton
,”, 2020
142020
Surrogate models for estimating failure in brittle and quasi-brittle materials
MK Mudunuru, N Panda, S Karra, G Srinivasan, VT Chau, E Rougier, ...
Applied Sciences 9 (13), 2706, 2019
14*2019
Using machine learning to discern eruption in noisy environments: A case study using CO2‐driven cold‐water Geyser in Chimayó, New Mexico
B Yuan, YJ Tan, MK Mudunuru, OE Marcillo, AA Delorey, PM Roberts, ...
Seismological Research Letters 90 (2A), 591-603, 2019
142019
Reduced order models to predict thermal output for enhanced geothermal systems
MK Mudunuru, S Karra, SM Kelkar, DR Harp, GD Guthrie Jr, ...
Los Alamos National Lab.(LANL), Los Alamos, NM (United States), 2019
142019
On local and global species conservation errors for nonlinear ecological models and chemical reacting flows
MK Mudunuru, M Shabouei, KB Nakshatrala
ASME International Mechanical Engineering Congress and Exposition 57526 …, 2015
122015
A machine learning framework for rapid forecasting and history matching in unconventional reservoirs
S Srinivasan, D O’Malley, MK Mudunuru, MR Sweeney, JD Hyman, ...
Scientific Reports 11 (1), 1-15, 2021
112021
Explore Spatio‐Temporal Learning of Large Sample Hydrology Using Graph Neural Networks
AY Sun, P Jiang, MK Mudunuru, X Chen
Water Resources Research 57 (12), e2021WR030394, 2021
92021
Machine learning to discover mineral trapping signatures due to CO2 injection
B Ahmmed, S Karra, VV Vesselinov, MK Mudunuru
International Journal of Greenhouse Gas Control 109, 103382, 2021
72021
A comparative study of machine learning models for predicting the state of reactive mixing
B Ahmmed, MK Mudunuru, S Karra, SC James, VV Vesselinov
Journal of Computational Physics 432, 110147, 2021
72021
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