BEGIN:VCALENDAR
VERSION:2.0
PRODID:IEEE vTools.Events//EN
CALSCALE:GREGORIAN
BEGIN:VTIMEZONE
TZID:America/Denver
BEGIN:DAYLIGHT
DTSTART:20240310T030000
TZOFFSETFROM:-0700
TZOFFSETTO:-0600
RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3
TZNAME:MDT
END:DAYLIGHT
BEGIN:STANDARD
DTSTART:20241103T010000
TZOFFSETFROM:-0600
TZOFFSETTO:-0700
RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11
TZNAME:MST
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20240725T200110Z
UID:03E475A8-8C47-4B2B-9ABB-08FCC4AFAD45
DTSTART;TZID=America/Denver:20240718T164500
DTEND;TZID=America/Denver:20240718T180000
DESCRIPTION:Abstract- The integration of physics-informed machine learning 
 (PIML) represents a paradigm shift in scientific and engineering research\
 , offering a powerful framework to address complex\, real-world problems b
 y embedding physical laws into data-driven models. Investigating the syner
 gy between traditional physics-based approaches and advanced machine learn
 ing techniques is important to enhance model efficiency\, interpretability
 \, and robustness. PIML has applications across various domains\, includin
 g fluid dynamics\, material science\, climate modeling\, and biomedical en
 gineering. In fluid dynamics\, PIML models demonstrate remarkable accuracy
  in predicting turbulent flows\, significantly advancing simulation capabi
 lities for aerospace and mechanical engineering applications. In material 
 science\, PIML accelerates the discovery of novel materials with optimized
  properties\, driving innovation in sectors such as electronics and renewa
 ble energy. Climate modeling benefits from the enhanced precision of PIML\
 , crucial for improving weather forecasts and addressing climate change im
 pacts. In biomedical engineering\, PIML enhances diagnostic accuracy and t
 reatment planning\, contributing to improved patient outcomes and healthca
 re efficiency.\n\nCo-sponsored by:  Brew with the Crew Informal Seminar Se
 ries - Sponsored by Eastern Idaho IEEE\, INL \, Idaho ISA\n\nStockman’s 
 Restaurant\, 1175 Pier View Drive\, Idaho Falls\, Idaho\, United States\, 
 83402-5039\, Virtual: https://events.vtools.ieee.org/m/427704
LOCATION:Stockman’s Restaurant\, 1175 Pier View Drive\, Idaho Falls\, Ida
 ho\, United States\, 83402-5039\, Virtual: https://events.vtools.ieee.org/
 m/427704
ORGANIZER:crnaik@ieee.org
SEQUENCE:10
SUMMARY:BWTC- Bridging Physics and Machine Learning: Real-World Application
 s
URL;VALUE=URI:https://events.vtools.ieee.org/m/427704
X-ALT-DESC:Description: <br /><p>Abstract- <span class="fontstyle0">The int
 egration of physics-informed machine learning (PIML) represents a paradigm
  shift in scientific and engineering research\, offering a powerful framew
 ork to address complex\, real-world problems by embedding physical laws in
 to data-driven models. Investigating the synergy between traditional physi
 cs-based approaches and advanced machine learning techniques is important 
 to enhance model efficiency\, interpretability\, and robustness. PIML has 
 applications across various domains\, including fluid dynamics\, material 
 science\, climate modeling\, and biomedical engineering. In fluid dynamics
 \, PIML models demonstrate remarkable accuracy in predicting turbulent flo
 ws\, significantly advancing simulation capabilities for aerospace and mec
 hanical engineering applications. In material science\, PIML accelerates t
 he discovery of novel materials with optimized properties\, driving innova
 tion in sectors such as electronics and renewable energy. Climate modeling
  benefits from the enhanced precision of PIML\, crucial for improving weat
 her forecasts and addressing climate change impacts. In biomedical enginee
 ring\, PIML enhances diagnostic accuracy and treatment planning\, contribu
 ting to improved patient outcomes and healthcare efficiency.</span>&nbsp\;
 </p>
END:VEVENT
END:VCALENDAR