Speaker
Description
This talk contextualizes EPICS in accelerator optimization and "quite
big" data experiment support, for other AI/ML session talks. EPICS 7
had the aim of better computing supporting for the next generation of
machines and analytical tools. Many new data types, such as matrices,
tables, images, and statistical descriptions, plus users' own data
types, now supplement the simple scalar and waveform types of the
classic EPICS. New EPICS based computational architectures for
scientific computing are emerging for high-performance data
processing, high fidelity archiving, pipelining, and classically
offline physics. Python and matlab bindings support ML and science
users. The result has been that controls are now being integrated with
multi-particle modelling, machine learning, enterprise databases,
experiment DAQs, and large legacy physics systems.
| Please select if talk will be in person or on zoom | In person |
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