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SUMMARY:Extremely Complex Problems in Metallurgy - Professor Sir Harry Bha
 deshia\, Department of Materials Science and Metallurgy\, University of Ca
 mbridge
DTSTART:20151117T183000Z
DTEND:20151117T200000Z
UID:TALK61873@talks.cam.ac.uk
CONTACT:Dr Geoff Hale
DESCRIPTION:Materials science differs from the pure subjects in that it at
 tempts problems at the level of complexity that is posed\, rather than by 
 simplification to study a narrow aspect. This raises challenges that usual
 ly involve interdisciplinary skills and myriads of non-linearly interactin
 g variables. A second distinction is that there is a genuine yearning to v
 alidate predictions. In this lecture I will introduce the method of neural
  networks within a Bayesian framework\, a method that is a form of mathema
 tical modelling that can help resolve complexity whilst striving for broad
 er solutions. I will demonstrate that the method permits the discovery of 
 new phenomena\, and the quantitative design of new materials with a minimu
 m use of resources. At the same time\, it introduces a culture in which bo
 th noise and modelling uncertainties are considered in order to realise th
 e value and limitations of the mathematical approach. Some recent successe
 s in the design of new materials include the -TRIP steel\, a welding alloy
  that cancels the development of residual stress\, and a nickel alloy that
  is cheap enough to serve in ultra-supercritical steam driven power plant.
LOCATION:Department of Materials Science and Metallurgy\, 27 Charles Babba
 ge Road\, Cambridge. CB3 0FS
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