On the Use of Supervised Learning Techniques to Speed up the Design of Aeronautics Components

author: Gianluca Bontempi, Department of Computer Science, Université Libre de Bruxelles
published: July 20, 2009,   recorded: July 2009,   views: 3209

Slides

Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.
  Bibliography

Description

A crucial issue in the design of complex systems is the evaluation of a large number of potential alternative designs. A too expensive evaluation procedure can consequently slow down the search for good configurations mainly in the case of high dimensional parameter spaces. The talk will discuss the use of machine learning techniques for speeding up the evaluation and the exploration of large design spaces. In particular, two supervised learning techniques, feedforward neural networks and lazy learning, are assessed and compared in the task of accelerating the design of a heat-pipe, a cooling device commonly used in aeronautics and electronics.

See Also:

Download slides icon Download slides: mla09_bontempi_otuosl_01.pdf (1.3¬†MB)


Help icon Streaming Video Help

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: