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BEGIN:VEVENT
DTEND;TZID=Europe/Zurich:20210309T190000
UID:be269aa4fb4a61fb72b4a11b289b5934
DTSTAMP:1777300693
LOCATION:ONLINE VIA ZOOM
ORGANIZER:Alumni Community of the FUSION-EP Master Program
DESCRIPTION:Automatic differentiation (AD) is a numerical technique for computing the derivative of a function specified as a computer program. Although AD was invented decades ago, it wasn’t until the recent interest in machine learning and the associated development of high-quality automatic differentiation frameworks that the benefits of AD in physics were more widely recognized. In this tutorial, I introduce AD. By the end of the tutorial, you will hopefully understand the fundamentals of how AD works in theory and how it is used in practice. For a short, 5-minute introduction to AD, feel free to read this https://twitter.com/NMcgreivy/status/1351706692317138945?s=20 and this https://twitter.com/NMcgreivy/status/1286057985987563525?s=20.
URL;VALUE=URI:https://fusionep-talks.egyplasma.com/calender/event.php?variableName=be269aa4fb4a61fb72b4a11b289b5934
SUMMARY:A tutorial on automatic differentiation for scientific design: practical, elegant and powerful
DTSTART;TZID=Europe/Zurich:20210309T180000
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