The application of machine learning (ML) to the computer simulation of materials has features that are somewhat uncommon in ML: the data is often free of noise, in principle unlimited amounts of data are available at known unit cost, and there is often considerable freedom in choosing data locations. This calls for the close examination of which ML strategies are best, and what their ultimate limitations are in practice. Can we create ML models of arbitrary accuracy? How can recent advances in on-line or active learning be utilized? What can more classical statistical interpolation methods contribute?
Application & Registration
The application form (See in the "Further official information" below this article) is for those requesting financial support to attend the workshop. We urge you to apply early. Applications received by Monday, October 10, 2016 will receive fullest consideration. Questions and supporting documents should be sent to the email below. Successful applicants will be notified as soon as funding decisions are made. If you do not need or want to apply for funding, you may simply register. IPAM will close registration if we reach capacity; for this reason, we encourage you to register early.
We have funding especially to support the attendance of recent PhD’s, graduate students, and researchers in the early stages of their career; however, mathematicians and scientists at all levels who are interested in this area are encouraged to apply for funding. Encouraging the careers of women and minority mathematicians and scientists is an important component of IPAM’s mission and we welcome their applications.
Please send your questions and supporting documents to: email@example.com
For further official information click " Further official information" below.