BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Weinan E (Princeton University) DTSTART:20201007T100000Z DTEND:20201007T110000Z DTSTAMP:20260423T003240Z UID:MPML/17 DESCRIPTION:Title: Ma chine Learning and Scientific Computing\nby Weinan E (Princeton Univer sity) as part of Mathematics\, Physics and Machine Learning (IST\, Lisbon) \n\n\nAbstract\nNeural network-based deep learning is capable of approxima ting functions in very high dimension with unprecedented efficiency and ac curacy. This has opened up many exciting new possibilities\, not just in t raditional areas of artificial intelligence\, but also in scientific compu ting and computational science. At the same time\, deep learning has also acquired the reputation of being a set of “black box” type of tricks\, without fundamental principles. This has been a real obstacle for making further progress in machine learning.\n\nIn this talk\, I will try to addr ess the following two questions:\n\n1. How machine learning will impact co mputational mathematics and computational science?\n\n2. How computational mathematics\, particularly numerical analysis\, can impact machine learni ng? We describe some of the most important progresses that have been made on these issues so far.\n\nOur hope is to put things into a perspective th at will help to integrate machine learning with computational science.\n LOCATION:https://researchseminars.org/talk/MPML/17/ END:VEVENT END:VCALENDAR