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  • Subsurface Energy and Environmental Systems (SEES) Lab

     

  • General Research Areas

    ResearchAreas

  • Subsurface Energy, Water, and Environmental ApplicationsApplication

  • Recent SEES Group Meeting

  • Complex High-Dimensional Geological Systems Effective description

  • Sparse Representations for Dimensionality Reduction
    Effective description

  • Inverse Modeling for Dynamic Data IntegrationKalman Filter

  • Data Science and Machine Learning for Subsurface Flow 

  • Model Predictive Control and Optimization for Field Operation and Management
    Optimization

  • Recent SEES Alumni

The research in the Subsurface Energy and Environmental Systems (SEES) lab is at the interface of AI and Machine Learning, Estimation and Control, and Energy Science and Engineering, to develop physics-informed AI solutions for energy and industrial applications. Our lab focuses on integrating Machine Learning with physics and domain knowledge to advance the state-of-the-art in inference, prediction, and control of complex subsurface energy and environmental systems. The research in our lab is sponsored by government, industry, and not-for-profit foundations. Within the Viterbi School of Engineering, we are affiliated with the Mork Family Department of Chemical Engineering and Materials Science, Ming Hsieh Departmental of Electrical Engineering, and Sonny Astani Departmental of Civil and Environmental Engineering.

University of Southern California