Charts
Uncertainty quantification for complex systems: theory and methodologiesEpisode from the podcastUncertainty quantification for complex systems: theory and methodologies
Next Episode
MSG Design of Experiments Seminar Series: Simulation-based Bayesian experimental design for computationally intensive models

MSG Design of Experiments Seminar Series: Simulation-based Bayesian experimental design for computationally intensive models

Released Friday, 22nd June 2018
Good episode? Give it some love!
MSG Design of Experiments Seminar Series: Simulation-based Bayesian experimental design for computationally intensive models

MSG Design of Experiments Seminar Series: Simulation-based Bayesian experimental design for computationally intensive models

MSG Design of Experiments Seminar Series: Simulation-based Bayesian experimental design for computationally intensive models

MSG Design of Experiments Seminar Series: Simulation-based Bayesian experimental design for computationally intensive models

Friday, 22nd June 2018
Good episode? Give it some love!
Rate Episode
List
About
InsightsPodchaser Pro
Reviews
Credits
Lists
Transcript

Huan, XWednesday 20th June 2018 - 14:55 to 15:45

Show More
Rate
List
Get this podcast via API

From The Podcast

In areas as diverse as climate modelling, manufacturing, energy, life sciences, finance, geosciences and medicine, mathematical models and their discretisations into computer models are routinely used to inform decisions, assess risk and formulate policies. How accurate are the predictions made using such models? This crucial question lies at the heart of uncertainty quantification (UQ).UQ is a broad phrase used to describe methodologies for taking account of uncertainties when mathematical and computer models are used to describe real-world phenomena. This includes propagating uncertainty from unknown model inputs to model outputs, the study of uncertainty in the models themselves, developing approximation schemes that result in tractable and accurate computer models, robust design, model calibration and other inverse problems, model bias and discrepancy etc. This programme focuses on UQ for complex systems which have complicated mathematical descriptions such as systems of partial differential equations for which even a single deterministic inversion of an associated computer model is very costly.The scientific challenges of modern life, the recent rapid growth in computing power and the demand for more accurate and precise predictions in areas affecting improved infrastructures, public safety and economic well-being have spawned a recent surge in UQ activity. New UQ methodologies have and are continuing to be developed by statisticians and applied mathematicians independently.The main aim of the programme is to bring applied mathematicians and statisticians together to formulate a common mathematical foundation for UQ and to establish long-lasting interactions that will lead to significant advances in UQ theory and methodologies for complex systems. Participants will work together to develop theories and methodologies for reducing the cost of model inversion, increasing the level of tractable complexity in modelling, and enabling efficient risk assessment and decision making. Five core themes of common interest to statisticians and applied mathematicians will provide the focus. These are:Surrogate modelsMultilevel, multi-scale, and multi-fidelity methodsDimension reduction methodsInverse UQ methodsCareful and fair comparisons

Join Podchaser to...

  • Rate podcasts and episodes
  • Follow podcasts and creators
  • Create podcast and episode lists
  • & much more
Do you host or manage this podcast?
Claim and edit this page to your liking.
,
Podchaser Logo

Podchaser is the ultimate destination for podcast data, search, and discovery. Learn More