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UID:20251127T165113EST-8558KkeeNO@132.216.98.100
DTSTAMP:20251127T215113Z
DESCRIPTION:Title: Robust sparse recovery techniques for high-dimensional f
 unction approximation\n\nAbstract:\n\nWe will consider the problem of comp
 uting sparse polynomial approximations of functions defined over high-dime
 nsional domains from pointwise samples\, primarily motivated by the uncert
 ainty quantification of PDEs with random inputs. In this context\, recentl
 y introduced techniques based on sparse recovery and on compressive sensin
 g are able to substantially lessen the curse of dimensionality\, thus enab
 ling the effective approximation of high-dimensional functions from small 
 datasets. We will illustrate rigorous error estimates for these approaches
  by focusing\, in particular\, on their robustness to unknown errors corru
 pting the data. Finally\, we will demonstrate their effectiveness through 
 numerical experiments and present some open challenges in the field.\n
DTSTART:20190909T200000Z
DTEND:20190909T210000Z
LOCATION:Room 1104\, Burnside Hall\, CA\, QC\, Montreal\, H3A 0B9\, 805 rue
  Sherbrooke Ouest
SUMMARY:Simone Brugiapaglia (Concordia)
URL:/mathstat/channels/event/simone-brugiapaglia-conco
 rdia-300201
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