AI RESEARCH
Deep Learning for Model Calibration in Simulation of Itaconic Acid Production
arXiv CS.LG
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ArXi:2604.22496v1 Announce Type: new In this study, deep learning is used to estimate kinetic parameters for modeling itaconic acid production based on real batch experiments conducted at different agitation speeds and reactor scales. Two deep learning strategies, namely direct deep learning (DDL) and generative conditional flow matching (CFM) are compared and benchmarked against nonlinear regression as a reference method. Compared with DDL, CFM consistently yields accurate results.