The phenomenon of arti¯cial cavitation is a signi¯cant and practical aspect of reducing drag forces on devices interacting with water. Among various regimes, the supercavity regime, where the entire body or a substantial portion of it is enveloped by a cavity, plays a crucial role. This study investigates the cavitation phenomenon using a combination of numerical experimental methods and machine learning techniques. Speci¯cally, the Support Vector Machine (SVM) classi¯cation model is employed to identify the type of arti¯cial cavitation regime and to determine the occurrence of the supercavity regime based on input variables. The ¯ndings indicate that the Radial Basis Function (RBF) model outperforms other machine learning models in accurately detecting the cavity regime type, achieving an accuracy of over 95% in identifying the supercavity regime. Additionally, results from the RBF model demonstrate that the supercavity regime is observed at low cavitation numbers, exhibiting the longest cavity length.

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