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Energy and Environment Directorate

Maruti Mudunuru

Pacific Northwest National Laboratory
PO Box 999
Richland, WA 99352
(509) 375-6645

PNNL Publications

2023

  • Jagtap N.V., M. Mudunuru, and K.B. Nakshatrala. 2023. "CoolPINNs: A Physics-informed Neural Network Modeling of Active Cooling in Vascular Systems." Applied Mathematical Modelling 122. PNNL-SA-175300. doi:10.1016/j.apm.2023.04.020
  • Jiang P., P. Shuai, A. Sun, M. Mudunuru, and X. Chen. 2023. "Knowledge-Informed Deep Learning for Hydrological Model Calibration: An Application to Coal Creek Watershed in Colorado." Hydrology and Earth System Sciences 27, no. 14:2621-2644. PNNL-SA-175917. doi:10.5194/hess-27-2621-2023
  • Mudunuru M., B. Ahmmed, and L. Frash. 2023. "GeoThermalCloud for EGS - An Open-source, User-friendly, Scalable AI Workflow for Modeling Enhanced Geothermal Systems." In Transactions - Geothermal Resources Council, October 1-4, 2023, Reno, NV, 47, 2334 - 233. Davis, California:Geothermal Resources Council. PNNL-SA-187504. GeoThermalCloud for EGS - An Open-source, User-friendly, Scalable AI Workflow for Modeling Enhanced Geothermal Systems
  • Mudunuru M., B. Ahmmed, E. Rau, V.V. Vesselinov, and S. Karra. 2023. "Machine Learning for Geothermal Resource Exploration in the Tularosa Basin, New Mexico." Energies 16, no. 7:Art. No. 3098. PNNL-SA-181184. doi:10.3390/en16073098
  • Mudunuru M., B. Ahmmed, L. Frash, and R.M. Frijhoff. 2023. "Deep Learning for Modeling Enhanced Geothermal Systems." In Proceedings of the 48th Workshop on Geothermal Reservoir Engineering, February 6-8, 2023, Stanford, CA, Paper No. SGP-TR-224. Stanford, California:Stanford University. PNNL-SA-181520.
  • Talsma C.J., K. Solander, M. Mudunuru, B.M. Crawford, and M.R. Powell. 2023. "Frost Prediction using Machine Learning and Deep Neural Network Models." Frontiers in Artificial Intelligence 5. PNNL-SA-180159. doi:10.3389/frai.2022.963781

2022

  • Hills D.J., J. Damerow, B. Ahmmed, N.K. Catolico, S. Chakraborty, C.M. Coward, and R. Crystal-Ornelas, et al. 2022. "Earth and Space Science Informatics Perspectives on Integrated, Coordinated, Open, Networked (ICON) Science." Earth and Space Science 9, no. 4:Art. No. e2021EA002108. PNNL-SA-167274. doi:10.1029/2021ea002108
  • Jagtap N.V., M. Mudunuru, and K.B. Nakshatrala. 2022. "A deep learning modeling framework to capture mixing patterns in reactive-transport systems." Communications in Computational Physics 31, no. 1:188-223. PNNL-SA-159450. doi:10.4208/cicp.OA-2021-0088
  • Mudunuru M., E. Cromwell, H. Wang, and X. Chen. 2022. "Deep Learning to Estimate Permeability using Geophysical Data." Advances in Water Resources 167. PNNL-SA-175440. doi:10.1016/j.advwatres.2022.104272
  • Mudunuru M., K. Son, P. Jiang, G.E. Hammond, and X. Chen. 2022. "Scalable Deep Learning for Watershed Model Calibration." Frontiers in Earth Science 10. PNNL-SA-176859. doi:10.3389/feart.2022.1026479
  • Mudunuru M., V.V. Vesselinov, and B. Ahmmed. 2022. "GeoThermalCloud: Machine Learning for Geothermal Resource Exploration." Journal of Machine Learning for Modeling and Computing 3, no. 4:57-72. PNNL-SA-178322. doi:10.1615/JMachLearnModelComput.2022046445
  • Vesselinov V.V., B. Ahmmed, M. Mudunuru, J.D. Pepin, E.R. Burns, D.L. Siler, and S. Karra, et al. 2022. "Discovering Hidden Geothermal Signatures using Non-Negative Matrix Factorization with Customized k-means Clustering." Geothermics 106. PNNL-SA-167650. doi:10.1016/j.geothermics.2022.102576

2021

  • Ahmmed B., M. Mudunuru, S. Karra, S.C. James, and V.V. Vesselinov. 2021. "A Comparative Study of Machine Learning Models for Predicting the State of Reactive Mixing." Journal of Computational Physics 432. PNNL-SA-157340. doi:10.1016/j.jcp.2021.110147
  • Ahmmed B., S. Karra, V.V. Vesselinov, and M. Mudunuru. 2021. "Machine Learning to Discover Mineral Trapping Signatures due to CO2 Injection." International Journal of Greenhouse Gas Control 109. PNNL-SA-158891. doi:10.1016/j.ijggc.2021.103382
  • Mudunuru M., and S. Karra. 2021. "Physics-Informed Machine Learning Models for Predicting the Progress of Reactive-Mixing." Computer Methods in Applied Mechanics and Engineering 374. PNNL-SA-157344. doi:10.1016/j.cma.2020.113560
  • Siler D.L., J.D. Pepin, V.V. Vesselinov, M. Mudunuru, and B. Ahmmed. 2021. "Machine learning to identify geologic factors associated with production in geothermal fields: A case-study using 3D geologic data, Brady geothermal field, Nevada." Geothermal Energy 9, no. 1:Article No. 17. PNNL-SA-158884. doi:10.1186/s40517-021-00199-8
  • Srinivasan S., D. O'Malley, M. Mudunuru, M. Sweeney, J.D. Hyman, S. Karra, and L. Frash, et al. 2021. "A machine learning framework for rapid forecasting and history matching in unconventional reservoirs." Scientific Reports 11, no. 1:Ar. No. 21730. PNNL-SA-159040. doi:10.1038/s41598-021-01023-w

2020

  • Ahmmed B., M. Mudunuru, S. Karra, S.C. James, H. Viswanathan, and J. Dunbar. 2020. "PFLOTRAN-SIP: A PFLOTRAN Module for Simulating Spectral-induced Polarization of Electrical Impedance Data." Energies 13, no. 24:6552. PNNL-SA-157259. doi:10.3390/en13246552

Energy and Environment

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