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Incomplete conditional density estimation for fast materials discovery

Designing new physical products and processes requires enormous experimentation. The scientific simulators play a fundamental role for such design tasks. To design a new product with certain target characteristics, a search is performed in the design space by trying out a large number of design combinations through simulators before reaching to the target characteristics. However, searching for the target design using simulators is generally expensive and becomes prohibitive when the target is either revised or only partially specified. To address this problem, we use a machine learning model to predict the design in single step using the target product specifications as input. We overcome two technical challenges: the first caused due to one-to-many mapping when learning the inverse problem and the second caused due to a user specifying the target specifications only partially. We unify a conditional variational auto-encoder model (to address the partial target specification) with mixture density networks (to address the one-to-many mapping) and train an end-to-end model to predict the optimum design.

History

Pagination

549-557

Location

Calgary, Alta.

Open access

  • Yes

Start date

2019-05-02

End date

2019-05-04

ISBN-13

9781611975673

Language

eng

Publication classification

E1 Full written paper - refereed

Copyright notice

2019, SIAM

Editor/Contributor(s)

Berger-Wolf T, Chawla N

Title of proceedings

2019 SIAM : Proceedings of the 2019 SIAM International Conference on Data Mining

Event

Society for Industrial and Applied Mathematics. Conference (2019 : Calgary, Alta.)

Publisher

Society for Industrial and Applied Mathematics

Place of publication

Philadelphia, Pa.

Series

Society for Industrial and Applied Mathematics Conference

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