| 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 | 31 | 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 | 25 | 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 | 22 | 2017 |
| 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 | 19 | 2012 |
| 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 | 17* | 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 | 15 | 2019 |
| 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 | 15* | 2017 |
| 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 | 15 | 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 | 15 | 2016 |
| 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 | 12 | 2019 |
| 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 | 11 | 2015 |
| 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 | 9 | 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 | 6* | 2019 |
| Scalable time-series feature engineering framework to understand multiphase flow using acoustic signals MK Mudunuru, VK Chillara, S Karra, D Sinha Proceedings of Meetings on Acoustics 6ICU 32 (1), 055003, 2017 | 3 | 2017 |
| Integration of data, numerical inversion, and unsupervised machine learning to identify hidden geothermal resources in southwest New Mexico B Ahmmed, M Mudunuru AGU Fall Meeting 2020, 2020 | 2 | 2020 |
| 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 | 2 | 2020 |
| Physics-informed machine learning models for predicting the progress of reactive-mixing MK Mudunuru, S Karra Computer Methods in Applied Mechanics and Engineering 374, 113560, 2019 | 2 | 2019 |
| IoGET: Internet of Geophysical and Environmental Things MK Mudunuru Los Alamos National Lab.(LANL), Los Alamos, NM (United States), 2017 | 2 | 2017 |
| A Comparative Study of Machine Learning Models for Predicting the State of Reactive Mixing B Ahmmed, MK Mudunuru, S Karra, SC James, VV Vesselinov arXiv preprint arXiv:2002.11511, 2020 | 1 | 2020 |
| Discovering signatures of hidden geothermal resources based on unsupervised learning VV Vesselinov, MK Mudunuru, B Ahmmed, S Karra, RS Middleton Los Alamos National Lab.(LANL), Los Alamos, NM (United States), 2020 | 1 | 2020 |
Satish KarraLos Alamos National LaboratoryVerified email at lanl.gov
