View source for Science-informed Machine Learning for Subsurface Applications

{{Program
|ProgramName=Science-informed Machine Learning for Subsurface Applications
|ProgramType=Program
|OrgSponsor=Office of Fossil Energy and Carbon Management
|TopOrganization=Department of Energy
|CreationLegislation=None
|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/
|ProgramStart=2020
|InitialFunding=$10 million
|Duration=Ongoing
|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. 

Led by the [[National Energy Technology Laboratory]] (NETL) with partners like Battelle and multiple universities, SMART integrates physics-informed ML (PIML) with field data from DOE-supported sites—e.g., carbon storage pilots—handling over 1 petabyte of data across its phases to advance tools for reservoir forecasting and virtual learning.<ref>{{cite web |url=https://edx.netl.doe.gov/smart/ |title=SMART Initiative |publisher=National Energy Technology Laboratory}}</ref> 

Phase 2, completed by 2023, emphasized deep learning for 3D spatiotemporal models, with Phase 3 (2024 onward) focusing on uncertainty quantification, building on over 20 years of DOE subsurface research.

{{Official URL (simple)|url=https://edx.netl.doe.gov/smart/}}