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Science-informed Machine Learning for Subsurface Applications: Difference between revisions

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|TopOrganization=Department of Energy
|TopOrganization=Department of Energy
|CreationLegislation=None
|CreationLegislation=None
|Purpose=The Science-informed Machine Learning for Subsurface Applications program develops AI and machine learning tools to enhance real-time decision-making for subsurface energy systems like carbon storage and geothermal energy. It aims to integrate physics-based models with data-driven approaches to improve forecasting, monitoring, and management of subsurface resources.
|Purpose=The SMART program develops AI and machine learning tools to enhance real-time decision-making for subsurface energy systems like carbon storage and geothermal energy. It aims to integrate physics-based models with data-driven approaches to improve forecasting, monitoring, and management of subsurface resources.
|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=false
|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.