BEGIN:VCALENDAR
VERSION:2.0
PRODID:IEEE vTools.Events//EN
CALSCALE:GREGORIAN
BEGIN:VTIMEZONE
TZID:America/Denver
BEGIN:DAYLIGHT
DTSTART:20260308T030000
TZOFFSETFROM:-0700
TZOFFSETTO:-0600
RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3
TZNAME:MDT
END:DAYLIGHT
BEGIN:STANDARD
DTSTART:20261101T010000
TZOFFSETFROM:-0600
TZOFFSETTO:-0700
RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11
TZNAME:MST
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20260416T225421Z
UID:C0D42AB0-67E5-4B08-8DC8-E4F787E328D1
DTSTART;TZID=America/Denver:20260415T180000
DTEND;TZID=America/Denver:20260415T203000
DESCRIPTION:This talk explores how AI-driven techniques — including machi
 ne learning — can enhance time-series prediction\, attractor reconstruct
 ion\, and digital twin development. Topics include supervised and unsuperv
 ised prediction of critical transitions in complex systems such as climate
  and power grids\, and data-driven model discovery using Kolmogorov-Arnold
  Networks.\n\nCase studies cover AI-assisted forecasting of the Atlantic M
 eridional Overturning Circulation\, and a vision for an ongoing project on
  physical AI addressing fundamental challenges in electrical and computer 
 engineering across multiple disciplines.\n\nSpeaker(s): Walt Slade\, \n\nA
 genda: \nDoors Open 6:00pm\n\nNetworking from 6:00-6:30pm\n\nAnnouncements
  and Welcome-6:30- 6:45 pm\, Jim Cale\n\nDid Y'know-6:45-7:00 pm- Scott Ev
 ans\n\nTechnical Presentation 7:00-8:00 pm\, Dr. Panahi\n\nA light snack w
 ill be available\, probably Pizza and Soft drinks\n\nRoom: B101\, Bldg: En
 gineering\, 400 Isotope Dr.\, Fort Collins\, Colorado\, United States\, 80
 525
LOCATION:Room: B101\, Bldg: Engineering\, 400 Isotope Dr.\, Fort Collins\, 
 Colorado\, United States\, 80525
ORGANIZER:rtoftness@gmail.com
SEQUENCE:30
SUMMARY:AI and Data-Driven Approaches for Forecasting\, Control\, and Model
  Discovery in Complex Engineering Systems
URL;VALUE=URI:https://events.vtools.ieee.org/m/553728
X-ALT-DESC:Description: <br /><div>&nbsp\;</div>\n<div>\n<p>This talk explo
 res how AI-driven techniques &mdash\; including machine learning &mdash\; 
 can enhance time-series prediction\, attractor reconstruction\, and digita
 l twin development. Topics include supervised and unsupervised prediction 
 of critical transitions in complex systems such as climate and power grids
 \, and data-driven model discovery using Kolmogorov-Arnold Networks.</p>\n
 <p>Case studies cover AI-assisted forecasting of the Atlantic Meridional O
 verturning Circulation\, and a vision for an ongoing project on&nbsp\;<em>
 physical AI</em>&nbsp\;addressing fundamental challenges in electrical and
  computer engineering across multiple disciplines.</p>\n</div><br /><br />
 Agenda: <br /><p>Doors Open 6:00pm</p>\n<p>Networking from 6:00-6:30pm</p>
 \n<p>Announcements and Welcome-6:30- 6:45 pm\, Jim Cale</p>\n<p>Did Y'know
 -6:45-7:00 pm- Scott Evans</p>\n<p>Technical Presentation 7:00-8:00 pm\, D
 r. Panahi</p>\n<p>A light snack will be available\, probably Pizza and Sof
 t drinks</p>\n<p>&nbsp\;</p>
END:VEVENT
END:VCALENDAR