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<span style="margin: 0px; font-size: 15px; background-color: white;">The AI Lunch Series will be hosting Dr. Adi Hanuka (SLAC) this Wednesday for a presentation on “<i><span style="margin:0px;font-size:12pt;line-height:17.12px;font-family:"Calibri Light", sans-serif">Machine
learning-based Design and Control of Particle Accelerators</span></i>”. Additional information can be found at: <a href="https://www.jlab.org/AI/lunch_series/2021" target="_blank" rel="noopener noreferrer" data-auth="NotApplicable" style="margin:0px">https://www.jlab.org/AI/lunch_series/2021</a></span>
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<div style="margin: 0px; font-size: 15px; background-color: white;">12-1PM, Wednesday, February 10, 2021</div>
<div style="margin: 0px; font-size: 15px; background-color: white;">Join with Bluejeans:<span style="margin:0px"> <a href="https://bluejeans.com/230468745" target="_blank" rel="noopener noreferrer" data-auth="NotApplicable" style="margin:0px">https://bluejeans.com/230468745</a></span></div>
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<div style="margin: 0px; font-size: 15px; background-color: white;">Abstract: Machine learning has been used in various ways to improve accelerator operation including advanced optimization of accelerator operating configurations, development of virtual diagnostics
to ’measure’ beam parameters, and prognostics to detect anomalies and predict failures. In this talk I'll review machine learning techniques currently being investigated at particle accelerator facilities, with specific emphasis on physics-informed active-learning
research efforts.<br>
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<span style="margin: 0px; font-size: 15px; background-color: white;">If you have questions, contact tennant@jlab.org </span><br>
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