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Science-informed Machine Learning for Subsurface Applications: Difference between revisions
Science-informed Machine Learning for Subsurface Applications (view source)
Revision as of 22:04, 13 March 2025
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|TopOrganization=Department of Energy | |TopOrganization=Department of Energy | ||
|CreationLegislation=None | |CreationLegislation=None | ||
|Purpose= | |Purpose=SMART, run by devs via AI & ML tools, enhances real-time decisions for subsurface energy like carbon storage & geothermal, integrating physics-based models with data-driven methods to boost forecasting, monitoring, & management. | ||
|Website=https://edx.netl.doe.gov/smart/ | |Website=https://edx.netl.doe.gov/smart/ | ||
|ProgramStart=2020 | |ProgramStart=2020 | ||
|InitialFunding=$10 million | |InitialFunding=$10 million | ||
|Duration=Ongoing | |Duration=Ongoing | ||
|Historic= | |Historic=No | ||
}} | }} | ||
The '''Science-informed Machine Learning for Subsurface Applications''' (SMART) initiative is a Department of Energy program launched in 2020 under the [[Office of Fossil Energy and Carbon Management]] (FECM) to harness artificial intelligence (AI) and machine learning (ML) for real-time subsurface energy management. | The '''Science-informed Machine Learning for Subsurface Applications''' (SMART) initiative is a Department of Energy program launched in 2020 under the [[Office of Fossil Energy and Carbon Management]] (FECM) to harness artificial intelligence (AI) and machine learning (ML) for real-time subsurface energy management. | ||