BEGIN:VCALENDAR
VERSION:2.0
PRODID:Linklings LLC
BEGIN:VTIMEZONE
TZID:Europe/Stockholm
X-LIC-LOCATION:Europe/Stockholm
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:19700308T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=-1SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:19701101T020000
RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=-1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20241120T082410Z
LOCATION:HG E 1.1
DTSTART;TZID=Europe/Stockholm:20240604T140000
DTEND;TZID=Europe/Stockholm:20240604T153000
UID:submissions.pasc-conference.org_PASC24_sess180@linklings.com
SUMMARY:AP2C - ACM Papers Session 2C
DESCRIPTION:Paper\n\nPETScML: Second-Order Solvers for Training Regression
  Problems in Scientific Machine Learning\n\nIn recent years, we have witne
 ssed the emergence of scientific machine learning as a data-driven tool fo
 r the analysis, by means of deep-learning techniques, of data produced by 
 computational science and engineering applications. <br /> At the core of 
 these methods is the supervised training algorit...\n\n\nStefano Zampini (
 King Abdullah University of Science and Technology), Umberto Zerbinati (Un
 iversity of Oxford), and George Turkyyiah and David Keyes (King Abdullah U
 niversity of Science and Technology)\n---------------------\nTopological I
 nterpretability for Deep Learning\n\nWith the growing adoption of AI-based
  systems across everyday life, the need to understand their decision-makin
 g mechanisms is correspondingly increasing. The level at which we can trus
 t the statistical inferences made from AI-based decision systems is an inc
 reasing concern, especially in high-risk ...\n\n\nAdam Spannaus, Heidi Han
 son, and Georgia Tourassi (Oak Ridge National Laboratory) and Lynne Penber
 thy (NIH)\n---------------------\nTowards Sobolev Pruning\n\nThe increasin
 g use of stochastic models for describing complex phenomena warrants surro
 gate models that capture the reference model characteristics at a fraction
  of the computational cost, foregoing potentially expensive Monte Carlo si
 mulation. The predominant approach of fitting a large neural netw...\n\n\n
 Neil Kichler, Sher Afghan, and Uwe Naumann (RWTH Aachen University)\n\nDom
 ain: Computational Methods and Applied Mathematics\n\nSession Chair: Luca 
 Muscarnera (Politecnico di Milano)
END:VEVENT
END:VCALENDAR