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SUMMARY:CSAR lecture: Physics IS Enhancing Machine Learning - Dr Alice Cic
 irello\, Data\, Vibration and Uncertainty Group\, Department of Engineerin
 g
DTSTART:20240527T183000Z
DTEND:20240527T200000Z
UID:TALK216868@talks.cam.ac.uk
CONTACT:John Cook
DESCRIPTION:Moving away from accurate-but-wrong predictions for bridges\, 
 wind turbines… and the climate.\nMachine Learning algorithms are revolut
 ionising many scientific fields by enabling the development of models from
  observations – so called data-driven. However\, in many engineering app
 lications\, we usually have access to a limited amount of “informative
 ” data - hindering the applicability data-driven approaches – but a gr
 eat deal of physics understanding and domain knowledge! This opens up oppo
 rtunities to combine physics and domain knowledge with data-driven approac
 hes for guiding high-consequence decision making in engineering applicatio
 ns.\n\nThis seminar will give a brief non-technical introduction to Machin
 e Learning and an overview of recent research work carried out within the 
 Data\, Vibration and Uncertainty Group (https://sites.google.com/view/dvug
 roup) focusing on developing Physics Enhanced Machine Learning (PEML) stra
 tegies in applied mechanics. It will showcase recent PEML methods develope
 d for tackling challenges in wind turbines\, bridges and structural joints
 \, and ongoing efforts for investigating climate repair strategies.\n\nOpe
 n to all. More details including a link for booking\,  "here":https://www.
 csar.org.uk/lectures/2023-2024/tbc_20240527/.
LOCATION:Location: Wolfson Lecture Theatre\, Churchill College\, and Zoom
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