BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Penn Engineering Events - ECPv6.15.18//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Penn Engineering Events
X-ORIGINAL-URL:https://seasevents.nmsdev7.com
X-WR-CALDESC:Events for Penn Engineering Events
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20210314T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20211107T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20220313T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20221106T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20230312T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20231105T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221114T100000
DTEND;TZID=America/New_York:20221114T120000
DTSTAMP:20260405T121704
CREATED:20221107T175439Z
LAST-MODIFIED:20221107T175439Z
UID:7840-1668420000-1668427200@seasevents.nmsdev7.com
SUMMARY:ESE Ph.D. Thesis Defense: "Analysis and Control of Neural Network Dynamical Systems"
DESCRIPTION:Integrating machine learning and control systems has achieved remarkable success in controlling complex dynamical systems such as autonomous vehicles. However\, the resulting controlled system often has a neural network (NN) in the loop which represents the system dynamics\, control policy\, or perception. The nonlinearity and large scale of NNs make it challenging to provide formal safety or stability guarantees for such learning-enabled systems. This thesis focuses on developing specialized numerical tools for efficiently analyzing NN dynamical systems and a novel robust model predictive control (MPC) framework that is promising for controlling NN dynamical systems with safety guarantees. In the first part of the thesis\, I demonstrate how to build a hierarchy of verification methods for isolated output range analysis of NNs\, closed-loop reachability analysis\, and closed-loop stability analysis of NN dynamical systems. In the second part\, I present a novel robust MPC method for uncertain linear dynamical systems with significantly reduced conservatism compared with existing baselines and discuss the possibility of combining NN verification tools and robust MPC for safe control of complex dynamical systems.
URL:https://seasevents.nmsdev7.com/event/ese-ph-d-thesis-defense-analysis-and-control-of-neural-network-dynamical-systems/
LOCATION:Raisler Lounge (Room 225)\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Dissertation or Thesis Defense
ORGANIZER;CN="Electrical and Systems Engineering":MAILTO:eseevents@seas.upenn.edu
END:VEVENT
END:VCALENDAR