Zobrazeno 1 - 10
of 100
pro vyhledávání: '"Velimir V. Vesselinov"'
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
Autor:
Bulbul Ahmmed, Velimir V. Vesselinov
Publikováno v:
Renewable Energy. 197:1034-1048
Publikováno v:
Journal of Machine Learning for Modeling and Computing. 3:47-70
Publikováno v:
PLoS ONE, Vol 13, Iss 3, p e0193974 (2018)
Factor analysis is broadly used as a powerful unsupervised machine learning tool for reconstruction of hidden features in recorded mixtures of signals. In the case of a linear approximation, the mixtures can be decomposed by a variety of model-free B
Externí odkaz:
https://doaj.org/article/5aeb4cde40784611a30efe1e62ab16e7
Publikováno v:
Second International Meeting for Applied Geoscience & Energy.
Publikováno v:
New Journal of Physics, vol 24, iss 10
Only a subset of degrees of freedom are typically accessible or measurable in real-world systems. As a consequence, the proper setting for empirical modeling is that of partially-observed systems. Notably, data-driven models consistently outperform p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5dfdf1a4ee352d8c6f2b3ff0963eb91f
Autor:
Jeffrey Bennett, Jonathan Ogland-Hand, Kyle Cox, Peter Johnson, Erin Middleton, Amelia Pompilio, Samanwita Samal, Carl Talsma, Velimir V. Vesselinov, Kevin Ellett, Richard Middleton
Publikováno v:
SSRN Electronic Journal.
Autor:
Daniel O'Malley, Boian S. Alexandrov, Satish Karra, Maruti Kumar Mudunuru, Velimir V. Vesselinov
Publikováno v:
Journal of Computational Physics. 395:85-104
Analysis of reactive-diffusion simulations requires a large number of independent model runs. For each high-fidelity simulation, inputs are varied and the predicted mixing behavior is represented by changes in species concentration. It is then requir
Drought is a pressing issue for the Colorado River Basin (CRB) due to the social and economic value of water resources in the region and the significant uncertainty of future drought under climate ...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::54fc8fb6727f3c93f4a2ae8426ac03e1
https://doi.org/10.1002/essoar.10508427.1
https://doi.org/10.1002/essoar.10508427.1