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DTSTAMP:20251229T164134Z
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DTSTART;TZID=America/New_York:20251211T120000
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DESCRIPTION:The rapid rise in power density and complexity of electronic sy
 stems has made thermal management a critical challenge for ensuring reliab
 ility\, performance\, and sustainability. Artificial Intelligence (AI) off
 ers transformative opportunities to address this challenge by enabling dat
 a-driven modeling\, optimization\, and predictive control of cooling syste
 ms. By integrating AI with experimental and physics-based approaches\, ada
 ptive models can be developed to capture transient thermal behaviors\, and
  optimize system-level energy efficiency. This forms the foundation for di
 gital twins\, virtual replicas that continuously interact with their physi
 cal counterparts to provide system specific real-time monitoring\, and dat
 a driven decision support. In this talk\, I will present recent and ongoin
 g research activities at ES2 Binghamton on AI-enabled thermal management d
 esign\, with emphasis on cooling solutions for high-power chips in data ce
 nters. I will further highlight how these developments serve as a pathway 
 towards creating digital twins\, dynamic virtual replicas that integrate r
 eal-time data\, physics\, and AI to enable system-level monitoring\, predi
 ction\, and optimization. Together\, these advancements pave the way for r
 eliable\, energy-efficient\, and sustainable electronic systems.\n\nCo-spo
 nsored by: Benson Chan\n\nSpeaker(s): Srihanth Rangarajan\n\nAgenda: \nSee
  LOCATION tab for WebEx info\n\nVirtual: https://events.vtools.ieee.org/m/
 507226
LOCATION:Virtual: https://events.vtools.ieee.org/m/507226
ORGANIZER:bchan@ieee.org
SEQUENCE:22
SUMMARY:AI for Thermal Management of Electronic Systems: A Pathway to Digit
 al Twins
URL;VALUE=URI:https://events.vtools.ieee.org/m/507226
X-ALT-DESC:Description: <br /><p class="MsoNormal" style="text-align: justi
 fy\;">The rapid rise in power density and complexity of electronic systems
  has made thermal management a critical challenge for ensuring reliability
 \, performance\, and sustainability. Artificial Intelligence (AI) offers t
 ransformative opportunities to address this challenge by enabling data-dri
 ven modeling\, optimization\, and predictive control of cooling systems. B
 y integrating AI with experimental and physics-based approaches\, adaptive
  models can be developed to capture transient thermal behaviors\, and opti
 mize system-level energy efficiency. This forms the foundation for digital
  twins\, virtual replicas that continuously interact with their physical c
 ounterparts to provide system specific real-time monitoring\, and data dri
 ven decision support. In this talk\, I will present recent and ongoing res
 earch 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 pathway towar
 ds creating digital twins\, dynamic virtual replicas that integrate real-t
 ime data\, physics\, and AI to enable system-level monitoring\, prediction
 \, and optimization. Together\, these advancements pave the way for reliab
 le\, energy-efficient\, and sustainable electronic systems.</p>\n<p>&nbsp\
 ;</p><br /><br />Agenda: <br /><p><span style="font-size: 18pt\;">See LOCA
 TION tab for WebEx info</span></p>
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