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SUMMARY:MechE Colloquium: From Physics to Machine Learning and Back: Appli
 cations in Engineered Systems
DTSTART:20250930T120000
DTEND:20250930T130000
DTSTAMP:20260412T221110Z
UID:76c7f3eb4ffda5fa0e433d19d861732018fd9b019f123ad8ca75d081
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Olga Fink\, Intelligent Maintenance and Operations Syst
 ems\, The Civil Engineering Institute\, ENAC\, EPFL\nAbstract: Deep lear
 ning has become an essential tool in many engineering applications. Howeve
 r\, its effectiveness is often limited by its reliance on large\, represen
 tative\, and well-labeled datasets. In contrast\, condition monitoring dat
 a from complex systems is typically sparse\, unlabeled\, and unrepresentat
 ive\, making it difficult to apply purely data-driven methods effectively.
  Moreover\, deep learning models often perform poorly in extrapolation sce
 narios\, which are common in engineering systems with long service lives a
 nd evolving operational regimes.\nTo address these limitations\, integrati
 ng physical laws and domain knowledge into deep learning frameworks has sh
 own significant potential. This presentation will explore a range of appro
 aches that integrate physics-based principles into machine learning models
 . Particular attention will be given to the use of structural inductive bi
 ases\, such as those introduced by physics-informed graph neural networks\
 , to improve model robustness\, generalization and extrapolation.\nFinally
 \, the talk will examine emerging methods in symbolic regression that aim 
 to close the loop between data-driven learning and physical understanding\
 , enabling the discovery of interpretable\, physics-consistent models from
  data.\n\nBiography: Olga Fink has been assistant professor at EPFL since
  March 2022\, heading the Intelligent Maintenance and Operations Systems (
 IMOS) laboratory.  Olga’s research focuses on Physics-Informed Machine 
 Learning\, Multi-Modal Learning\, Domain Adaptation and Generalization\, a
 nd Reinforcement Learning for Intelligent Maintenance and Operations of In
 frastructure and Complex Assets.\nBefore joining EPFL faculty\, Olga was a
 ssistant professor of intelligent maintenance systems at ETH Zurich from 2
 018 to 2022\, being awarded the prestigious professorship grant of the Swi
 ss National Science Foundation (SNSF). Between 2014 and 2018 she was headi
 ng the research group “Smart Maintenance” at the Zurich University of 
 Applied Sciences (ZHAW).\nOlga received her Ph.D. degree from ETH Zurich\,
  and Diploma degree from Hamburg University of Technology. She has gained 
 valuable industrial experience as reliability engineer with Stadler Bussna
 ng AG and as reliability and maintenance expert with Pöyry Switzerland Lt
 d.\nOlga is serving as an editorial board member of several prestigious jo
 urnals\, including Mechanical Systems and Signal Processing\, Engineering 
 Applications of Artificial Intelligence and Reliability Engineering and Sy
 stem Safety.\nIn 2019\, Olga earned the distinction of being recognized as
  a young scientist of the World Economic Forum. In 2020\, 2021\, and 2024 
 she was honored as a young scientist of the World Laureate Forum. In 2023\
 , she was distinguished as a fellow by the Prognostics and Health Manageme
 nt Society.
LOCATION:CM 1 4 https://plan.epfl.ch/?room==CM%201%204 https://epfl.zoom.u
 s/j/61360740951
STATUS:CONFIRMED
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