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SUMMARY:Stay flexible\, get lucky: nonlinear approximation and random samp
 ling meet scientific machine learning - Simone Brugiapaglia (Concordia Uni
 versity)
DTSTART:20260326T150000Z
DTEND:20260326T160000Z
UID:TALK242584@talks.cam.ac.uk
CONTACT:Georg Maierhofer
DESCRIPTION:Nonlinear approximation and random sampling are two vital math
 ematical pillars of machine learning. On the one hand\, nonlinear approxim
 ation provides flexible models\, such as sparse polynomials or deep neural
  networks\, able to accurately represent very complex functions. On the ot
 her hand\, random sampling allows us to solve data-starved inverse problem
 s via\, e.g.\, compressive sensing. In recent years\, these tools have bee
 n frequently employed to tackle challenging problems in scientific computi
 ng within the research field now known as scientific machine learning. In 
 this talk\, I will review recent advances in this area by showcasing resul
 ts in high-dimensional approximation\, surrogate modelling\, and PDE solve
 rs. Throughout the talk\, the emphasis will be on numerical techniques acc
 ompanied by rigorous mathematical guarantees of performance.
LOCATION:Centre for Mathematical Sciences\, MR14
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