Deep Learning Models for real-time Fusion Device Data Compression Algorithms

The recently enhanced RFX-mod2 experiment, located at Consorzio RFX in Padova, presents a set of distinctive prospects for the advancement and validation of cutting-edge ML and DL algorithms and techniques, for plasma control. RFX-mod2 operates as a multi-configuration device, generating plasmas across various magnetic configurations: tokamak, ultra-low-q, and reversed field pinch (RFP). Among the peculiar features of RFX-mod2, it will provide a very high spatial resolution magnetic diagnostic with more than 1700 sensors, along with more than 200 actuator coils independently controlled. However, the overall throughput required for the complete transfer of information from the sensors to the central control system cannot be handled in real-time. The compromise applied so far is a dual-channel acquisition: one channel for low-latency, low-bandwidth data acquisition, specifically designed for the control system, and a second channel for full-resolution data. The second channel takes advantage of the transient nature of the experimental setup by buffering the data locally and storing all the acquired raw data on the central acquisition server after the pulse. However, the useful information within the signals acquired by both channels is rich only for very short periods, resulting in large amounts of data that are mostly noise for the rest of the pulse. Additionally, most of the non-zero information signals can actually be modeled by a composition of known response functions. This thesis focuses on the application of time series compression algorithms, specifically trained with the historical information acquired by the full length row signals in the RFX database, at the edge of the sensor devices.