BEGIN:VCALENDAR VERSION:2.0 PRODID:-//jEvents 2.0 for Joomla//EN CALSCALE:GREGORIAN METHOD:PUBLISH BEGIN:VTIMEZONE TZID:America/New_York BEGIN:STANDARD DTSTART:20200302T103000 RDATE:20200308T030000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:America/New_York EST END:STANDARD BEGIN:STANDARD DTSTART:20201101T010000 RDATE:20210314T030000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:America/New_York EST END:STANDARD BEGIN:STANDARD DTSTART:20211107T010000 RDATE:20220313T030000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:America/New_York EST END:STANDARD BEGIN:STANDARD DTSTART:20221106T010000 RDATE:20230312T030000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:America/New_York EST END:STANDARD BEGIN:STANDARD DTSTART:20231105T010000 RDATE:20240310T030000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:America/New_York EST END:STANDARD BEGIN:STANDARD DTSTART:20241103T010000 RDATE:20250309T030000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:America/New_York EST END:STANDARD BEGIN:STANDARD DTSTART:20251102T010000 RDATE:20260308T030000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:America/New_York EST END:STANDARD BEGIN:STANDARD DTSTART:20261101T010000 RDATE:20270314T030000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:America/New_York EST END:STANDARD BEGIN:STANDARD DTSTART:20271107T010000 RDATE:20280312T030000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:America/New_York EST END:STANDARD BEGIN:STANDARD DTSTART:20281105T010000 RDATE:20290311T030000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:America/New_York EST END:STANDARD BEGIN:DAYLIGHT DTSTART:20200308T030000 RDATE:20201101T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20210314T030000 RDATE:20211107T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20220313T030000 RDATE:20221106T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20230312T030000 RDATE:20231105T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20240310T030000 RDATE:20241103T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20250309T030000 RDATE:20251102T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20260308T030000 RDATE:20261101T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20270314T030000 RDATE:20271107T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20280312T030000 RDATE:20281105T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:1516d78876003f8fe699615cead5f62f CATEGORIES:D'Atri Memorial Lectures, Mathematical Physics Seminar CREATED:20210226T161803 SUMMARY:How Materials Can Learn How to Function LOCATION:zoom DESCRIPTION: Andrea Liu - University of Pennsylvania\n Wednesday, March 3, 10:45AM\n “H ow Materials Can Learn How to Function”\n Abstract: How does learning occur ? In the context of neural networks, learning occurs via optimization, wher e a loss function is minimized to achieve the desired result. But physical networks such as mechanical spring networks or flow networks cannot minimiz e such a loss function by themselves—they need the help of a computer. An a lternative is to encode local rules into those networks so that they can ev olve under external driving to develop function. For example, if the spring s in a mechanical network have equilibrium lengths that grow if the springs are stretched, and shrink when the springs are compressed, the network wil l naturally evolve under applied stresses. I will describe how both of thes e strategies—global minimization of a loss function as well as training by local rules--can be used to teach materials how to perform functions inspir ed by biology, such as the ability of proteins (e.g. hemoglobin) to change their conformations upon binding of an atom (oxygen) or molecule, or the ab ility of the brain’s vascular network to send enhanced blood flow and oxyge n to specific areas of the brain associated with a given task.\n \n X-ALT-DESC;FMTTYPE=text/html:

< span style="font-size: medium;">Andrea Liu - University of Pennsylvania

Wednesday, March 3, 10:45AM

“How Materials Can Learn How to Function”

Abstract: How does learning occur? In the context of neural networks, learning occurs via optimization, where a loss function i s minimized to achieve the desired result. But physical networks such as me chanical spring networks or flow networks cannot minimize such a loss funct ion by themselves—they need the help of a computer. An alternative is to en code local rules into those networks so that they can evolve under external driving to develop function. For example, if the springs in a mechanical n etwork have equilibrium lengths that grow if the springs are stretched, and shrink when the springs are compressed, the network will naturally evolve under applied stresses. I will describe how both of these strategies—global minimization of a loss function as well as training by local rules--can be used to teach materials how to perform functions inspired by biology, such as the ability of proteins (e.g. hemoglobin) to change their conformations upon binding of an atom (oxygen) or molecule, or the ability of the brain’ s vascular network to send enhanced blood flow and oxygen to specific areas of the brain associated with a given task.

CONTACT:Andrea Liu - University of Pennsylvania DTSTAMP:20260829T062134 DTSTART;TZID=America/New_York:20210303T103000 DTEND;TZID=America/New_York:20210303T113000 SEQUENCE:0 TRANSP:OPAQUE END:VEVENT END:VCALENDAR