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SUMMARY:Neural Inversion for Data-Driven Functional Fabrication
DTSTART;TZID=Europe/Berlin:20220706T140000
DTEND;TZID=Europe/Berlin:20220706T150000
DTSTAMP:20260411T023517Z
UID:574ba4b323124bfdb76fe0a77403895f@www.informatik.uni-bonn.de
CREATED:20220819T062742Z
DESCRIPTION:While digital manufacturing (e.g.\, 3D printing) has had signi
 ficant advances on the hardware and material fronts\, algorithmic design f
 or digital manufacturing remains one of the biggest barriers to its widesp
 read adoption. Computational Design and Fabrication (CDF) is an emerging r
 esearch area that tries to address this problem. Functional fabrication is
  an important paradigm in CDF where given the high-level function or goal\
 , the design is computed\, and physically realized by the fabrication hard
 ware. Although the functional fabrication lends itself to powerful abstrac
 tions\, we still lack a general and practical workflow. Here\, I will show
  how machine learning methods can raise these abstractions closer to the p
 ractical level. Specifically\, I will present the subject of neural invers
LAST-MODIFIED:20231214T225637Z
URL:https://www.informatik.uni-bonn.de/de/lost-found/veranstaltungen/neura
 l-inversion-for-data-driven-functional-fabrication
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DTSTART:20220327T030000
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