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With the ever-increasing availability of data, there has been an explosion ofinterest in applying modern machine learning methods to fields such as modelingand control. However, despite the flexibility and surprising accuracy of suchblack-box models, it remains difficult to trust them. Recent efforts to combinethe two approaches aim to develop flexible models that nonetheless generalizewell; a paradigm we call Hybrid Analysis and modeling (HAM). In this work weinvestigate the Corrective Source Term Approach (CoSTA), which uses adata-driven model to correct a misspecified physics-based model. This enablesus to develop models that make accurate predictions even when the underlyingphysics of the problem is not well understood. We apply CoSTA to model theHall-H\'eroult process in an aluminum electrolysis cell. We demonstrate thatthe method improves both accuracy and predictive stability, yielding an overallmore trustworthy model.

A novel corrective-source term approach to modeling unknown physics in aluminum extraction process