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DTSTART:20260308T030000
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DTSTART:20251102T010000
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DTSTAMP:20251001T160836Z
UID:B13948AE-EAE8-4C0C-B2EA-3B075F663344
DTSTART;TZID=America/Los_Angeles:20251211T090000
DTEND;TZID=America/Los_Angeles:20251211T100000
DESCRIPTION:[]The rapid rise in power density and complexity of electronic 
 systems has made thermal management a critical challenge for ensuring reli
 ability\, performance\, and sustainability. Artificial Intelligence (AI) o
 ffers transformative opportunities to address this challenge by enabling d
 ata-driven modeling\, optimization\, and predictive control of cooling sys
 tems. By integrating AI with experimental and physics-based approaches\, a
 daptive models can be developed to capture transient thermal behaviors\, a
 nd optimize system-level energy efficiency. This forms the foundation for 
 digital twins\, virtual replicas that continuously interact with their phy
 sical counterparts to provide system specific real-time monitoring\, and d
 ata driven decision support. In this talk\, I will present recent and ongo
 ing research activities at ES2 Binghamton on AI-enabled thermal management
  design\, with emphasis on cooling solutions for high-power chips in data 
 centers. I will further highlight how these developments serve as a pathwa
 y towards creating digital twins\, dynamic virtual replicas that integrate
  real-time data\, physics\, and AI to enable system-level monitoring\, pre
 diction\, and optimization. Together\, these advancements pave the way for
  reliable\, energy-efficient\, and sustainable electronic systems.\n\nSpea
 ker(s): Srikanth Rangarajan\, \n\nVirtual: https://events.vtools.ieee.org/
 m/504510
LOCATION:Virtual: https://events.vtools.ieee.org/m/504510
ORGANIZER:p.wesling@ieee.org
SEQUENCE:16
SUMMARY:AI for Thermal Management of Electronic Systems: A Pathway to Digit
 al Twins
URL;VALUE=URI:https://events.vtools.ieee.org/m/504510
X-ALT-DESC:Description: &lt;br /&gt;&lt;p&gt;&lt;img style=&quot;float: right\;&quot; src=&quot;https://e
 vents.vtools.ieee.org/vtools_ui/media/display/995d6381-8098-4607-a4f2-6fa8
 1f644fe6&quot; alt=&quot;&quot; width=&quot;448&quot; height=&quot;205&quot;&gt;The rapid rise in power density 
 and complexity of electronic systems has made thermal management a critica
 l challenge for ensuring reliability\, performance\, and sustainability. A
 rtificial Intelligence (AI) offers transformative opportunities to address
  this challenge by enabling data-driven modeling\, optimization\, and pred
 ictive control of cooling systems. By integrating AI with experimental and
  physics-based approaches\, adaptive models can be developed to capture tr
 ansient thermal behaviors\, and optimize system-level energy efficiency. T
 his forms the foundation for digital twins\, virtual replicas that continu
 ously interact with their physical counterparts to provide system specific
  real-time monitoring\, and data driven decision support. In this talk\, I
  will present recent and ongoing research activities at ES2 Binghamton on 
 AI-enabled thermal management design\, with emphasis on cooling solutions 
 for high-power chips in data centers. I will further highlight how these d
 evelopments serve as a pathway towards creating digital twins\, dynamic vi
 rtual replicas that integrate real-time data\, physics\, and AI to enable 
 system-level monitoring\, prediction\, and optimization. Together\, these 
 advancements pave the way for reliable\, energy-efficient\, and sustainabl
 e electronic systems.&lt;/p&gt;
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