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SUMMARY:ENAC Seminar Series by Dr A. Olivier
DTSTART:20190923T160000
DTEND:20190923T170000
DTSTAMP:20260924T085432Z
UID:36b2dde66f7df9c3a8b04b3dddfcc4b4b9ce9ceaa0dba9b5982c4b81
CATEGORIES:Conferences - Seminars
DESCRIPTION:Dr Audrey Olivier\n16:00 – 17:00 – Dr Audrey Olivier\nPost
 doctoral Research Scientist\, Johns Hopkins University\, USA\n\nData-assis
 ted high-fidelity modeling for monitoring of civil systems\n\nIncreased av
 ailability of measured data has recently generated tremendous interest in 
 the development of methods to learn from data. In parallel\, engineers hav
 e a long history of building high-fidelity physics-based models that allow
  us to model the behavior of highly complex systems. At the intersection o
 f these two topics\, data analytics and high-fidelity modeling\, lie excit
 ing opportunities to address the challenges of modern civil engineering.\n
 \nMonitoring the health of our aging or historic infrastructure and predic
 ting its response to future events is a challenging task\, in part due to 
 the presence of various uncertainties in the inputs\, measurements and the
  system itself. Probabilistic system identification methods such as Bayesi
 an inference use measurements from a system to learn its equations and par
 ameters\, thus allowing detection of potential damage\, while accounting f
 or the various uncertainties. However\, Bayesian techniques become computa
 tionally expensive for inference in large dimensional nonlinear systems\, 
 i.e.\, finite element models. We demonstrate the potential of Bayesian fil
 tering techniques and algorithmic enhancements to reduce computational cos
 t\, and how to integrate these probabilistic learning algorithms into comp
 lex frameworks of model selection and optimal design of experiments.\n\nWh
 ether it relates to monitoring the health of our infrastructure\, improvin
 g its resilience to natural disasters or designing the smart cities of tom
 orrow\, data sensing and analysis is becoming an integrative part of civil
  engineering research and practice. Very interestingly\, the combination o
 f data-mining and physics-based modeling also finds applications in variou
 s engineering fields. In the materials sciences for instance\, interesting
  opportunities lie in the development of scientific machine learning to sp
 eed-up materials discovery. Research in this field could thus benefit from
  and impact various fields of science and engineering.\n 
LOCATION:INJ 218 https://plan.epfl.ch/?room==INJ%20218
STATUS:CONFIRMED
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