| 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 | 52* | 2019 |
| 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 | 47 | 2019 |
| 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 | 43 | 2021 |
| 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 | 37 | 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 | 34 | 2016 |
| 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), 21730, 2021 | 33 | 2021 |
| 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 | 32 | 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, 1-17, 2021 | 29 | 2021 |
| 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, 1847-1885, 2016 | 27 | 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 | 26 | 2012 |
| 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 | 24* | 2020 |
| 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 | 23 | 2017 |
| Discovering signatures of hidden geothermal resources based on unsupervised learning VV Vesselinov, MK Mudunuru, B Ahmmed, S Karra, RS Middleton Proceedings, forty-fifth workshop on geothermal reservoir engineering …, 2020 | 20 | 2020 |
| 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 | 20 | 2019 |
| 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 | 18* | 2019 |
| 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 | 18 | 2017 |
| Frost prediction using machine learning and deep neural network models CJ Talsma, KC Solander, MK Mudunuru, B Crawford, MR Powell Frontiers in Artificial Intelligence 5, 963781, 2023 | 17 | 2023 |
| 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 | 16 | 2019 |
| Knowledge-informed deep learning for hydrological model calibration: An application to coal creek watershed in Colorado P Jiang, P Shuai, A Sun, MK Mudunuru, X Chen Hydrology and Earth System Sciences Discussions 2022, 1-31, 2022 | 14 | 2022 |
| 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 | 13 | 2021 |
Satish KarraEMSL/PNNL在 pnnl.gov 的电子邮件经过验证
