SPONSOR:
DOE (through subcontract with Sandia National Laboratories)
PROJECT PERIOD:
05/2020 – 09/2021
ABSTRACT:
We will develop and apply deep machine learning methods to predict CO2 flow and pressure distribution for geologic carbon storage. In particular, we will pursue advanced methods to incorporate real-time observation data into machine learning prediction. The proposed method will allow fast and reliable data assimilation of spatial and temporal observations.
PRINCIPAL INVESTIGATOR
JONGHYUN “HARRY” LEEAssociate Professor
One can benefit from applied mathematics statistics, and computer programming to understand underlying natural phenomena and perform relevant projects successfully.
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