Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Maruti K. Mudunuru"'
Autor:
Bulbul Ahmmed, Elisabeth G. Rau, Maruti K. Mudunuru, Satish Karra, Joshua R. Tempelman, Adam J. Wachtor, Jean-Baptiste Forien, Gabe M. Guss, Nicholas P. Calta, Phillip J. DePond, Manyalibo J. Matthews
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-14 (2024)
Abstract In additive manufacturing (AM), process defects such as keyhole pores are difficult to anticipate, affecting the quality and integrity of the AM-produced materials. Hence, considerable efforts have aimed to predict these process defects by t
Externí odkaz:
https://doaj.org/article/f0eabfad36394d9dbc93e354f8730b5a
Publikováno v:
Frontiers in Artificial Intelligence, Vol 5 (2023)
This study describes accurate, computationally efficient models that can be implemented for practical use in predicting frost events for point-scale agricultural applications. Frost damage in agriculture is a costly burden to farmers and global food
Externí odkaz:
https://doaj.org/article/890bf93454734753aff9281d3add66b7
A machine learning framework for rapid forecasting and history matching in unconventional reservoirs
Autor:
Shriram Srinivasan, Daniel O’Malley, Maruti K. Mudunuru, Matthew R. Sweeney, Jeffrey D. Hyman, Satish Karra, Luke Frash, J. William Carey, Michael R. Gross, George D. Guthrie, Timothy Carr, Liwei Li, Hari S. Viswanathan
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
Abstract We present a novel workflow for forecasting production in unconventional reservoirs using reduced-order models and machine-learning. Our physics-informed machine-learning workflow addresses the challenges to real-time reservoir management in
Externí odkaz:
https://doaj.org/article/19a3100ba9fc42afae2d745226851b22
Publikováno v:
Frontiers in Earth Science, Vol 10 (2022)
Watershed models such as the Soil and Water Assessment Tool (SWAT) consist of high-dimensional physical and empirical parameters. These parameters often need to be estimated/calibrated through inverse modeling to produce reliable predictions on hydro
Externí odkaz:
https://doaj.org/article/228a2990ea8b47559c18c9ddb71dfbf6
Publikováno v:
Geothermal Energy, Vol 9, Iss 1, Pp 1-17 (2021)
Abstract In this paper, we present an analysis using unsupervised machine learning (ML) to identify the key geologic factors that contribute to the geothermal production in Brady geothermal field. Brady is a hydrothermal system in northwestern Nevada
Externí odkaz:
https://doaj.org/article/5388e9f2a4b2497f8780ab76eaa5d803
Publikováno v:
Energies, Vol 16, Iss 7, p 3098 (2023)
Geothermal energy is considered an essential renewable resource to generate flexible electricity. Geothermal resource assessments conducted by the U.S. Geological Survey showed that the southwestern basins in the U.S. have a significant geothermal po
Externí odkaz:
https://doaj.org/article/74a203de35394ed5b401fabcf50c773e
Publikováno v:
Water Resources Research. 58
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7273a73eac97c863644f90884391f189
https://doi.org/10.1029/2022wr032932
https://doi.org/10.1029/2022wr032932
Publikováno v:
Second International Meeting for Applied Geoscience & Energy.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dd2935c88b5b470a9d0a219e8e0ff8cf
https://doi.org/10.1190/image2022-w8-01.1
https://doi.org/10.1190/image2022-w8-01.1
Deep learning (DL)-assisted inverse mapping has shown promise in hydrological model calibration by directly estimating parameters from observations. However, the increasing computational demand for running the state-of-the-art hydrological model limi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::860d600326f49378edff9eeb6c4d3109
https://doi.org/10.5194/hess-2022-282
https://doi.org/10.5194/hess-2022-282
Publikováno v:
SSRN Electronic Journal.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::df977973e61c05dcd377eefc4799a62a
https://doi.org/10.2139/ssrn.4032447
https://doi.org/10.2139/ssrn.4032447