Maruti Kumar Mudunuru
Hivatkozott rá
Hivatkozott rá
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Discovering signatures of hidden geothermal resources based on unsupervised learning
VV Vesselinov, MK Mudunuru, B Ahmmed, S Karra, RS Middleton
,”, 2020
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
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
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
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
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
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Cikkek 1–20