Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in Machine Learning. According to news reporting out of Karlsruhe, Germany, by NewsRx edit ors, research stated, “Including uncertainty is essential for accurate decision- making in underground applications. We propose a novel approach to consider stru ctural uncertainty in two enhanced geothermal systems (EGSs) using machine learn ing (ML) models.” Financial support for this research came from Deutscher Akademischer Austausch D ienst (DAAD). Our news journalists obtained a quote from the research from the Karlsruhe Insti tute of Technology (KIT), “The results of numerical simulations show that a smal l change in the structural model can cause a significant variation in the tracer breakthrough curves (BTCs). To develop a more robust method for including struc tural uncertainty, we train three different ML models: decision tree regression (DTR), random forest regression (RFR), and gradient boosting regression (GBR). D TR and RFR predict the entire BTC at once, but they are susceptible to overfitti ng and underfitting. In contrast, GBR predicts each time step of the BTC as a se parate target variable, considering the possible correlation between consecutive time steps. This approach is implemented using a chain of regression models. Th e chain model achieves an acceptable increase in RMSE from train to test data, c onfirming its ability to capture both the general trend and small-scale heteroge neities of the BTCs. Additionally, using the ML model instead of the numerical s olver reduces the computational time by six orders of magnitude. This time effic iency allows us to calculate BTCs for 2 ‘ 000 different reservoir models, enabli ng a more comprehensive structural uncertainty quantification for EGS cases.”
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