About this event
The computational cost of industrial-scale models can cause problems when performing sampling-based reliability analysis. This is due to the fact that the failure modes of engineering systems typically occupy a small region of the performance space and thus require relatively large sample sizes to accurately estimate their characteristics.
This talk explored two methods for reducing the cost of reliability analysis whilst preserving the accuracy of estimated quantities. The first approach, based on Markov chain Monte Carlo sampling, can be used when several thousands of code evaluations are available. The second method, built on the ideas of Gaussian process-based optimisation, lowers this requirement from tens to hundreds of evaluations.
The Optimisation Working Group has formed an online Community to help disseminate best practice and encourage the adoption of optimisation methods and technology. More information can be found on the Optimisation Community webpage.
The Optimisation Community is only accessible to NAFEMS members and no significant knowledge or expertise is required to participate. The only requirement is a desire to learn more and to interact with other engineers and scientists who have an interest in expanding their capabilities in the optimisation technical area.
Join the Optimisation Community
This webinar is available to all NAFEMS members exclusively as part of their membership.
Dr Peter Hristov, University of Liverpool
Peter Hristov is the PDRA on DATA-CENTRIC, an EPSRC fellowship project which aims to develop transparent and accountable computational engineering models. He holds a PhD in computational engineering and uncertainty quantification, and a bachelor’s degree in aerospace engineering from the University of Liverpool.
Dr. Hristov is currently affiliated with the Institute for Risk and Uncertainty at the University of Liverpool. His research interests lie in developing uncertainty-aware models for aerospace and structural applications. He’s actively engaging in bridging the gap between academia and industry.