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Computational Methods for Optical De...

Gannon, Caleb.

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Computational Methods for Optical Design.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Computational Methods for Optical Design./
作者:	Gannon, Caleb.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	147 p.
附註:	Source: Dissertations Abstracts International, Volume: 82-02, Section: B.
Contained By:	Dissertations Abstracts International82-02B.
標題:	Optics. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28027791
ISBN:	9798662487277

Computational Methods for Optical Design.
Gannon, Caleb.

Computational Methods for Optical Design. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 147 p.

Source: Dissertations Abstracts International, Volume: 82-02, Section: B.

Thesis (Ph.D.)--The University of Arizona, 2020.

This item must not be sold to any third party vendors.

An overview of computational methods for solving geometrical optical design problems is presented. Starting with a description of the illumination design problem within an optimal transport framework, a general algorithm for constructing freeform illumination optics directly from a source and target light distribution is explored. To accompany that, an analytic derivation of theoretical optical efficiency for lens systems is constructed which can improve throughput efficiency by over 150\\%. Then, a complete description of the imaging design problem is presented with some novel algorithmic approaches to solving it including an automated evolutionary sparse optimization algorithm and a machine learning approach. Some description of accelerated raytracing algorithms to speed up these methods will be presented as well.

ISBN: 9798662487277Subjects--Topical Terms:

517925
Optics.
Subjects--Index Terms:

Computation

Computational Methods for Optical Design.
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500 $a Source: Dissertations Abstracts International, Volume: 82-02, Section: B.
500 $a Advisor: Liang, Rongguang.
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506 $a This item must not be sold to any third party vendors.
520 $a An overview of computational methods for solving geometrical optical design problems is presented. Starting with a description of the illumination design problem within an optimal transport framework, a general algorithm for constructing freeform illumination optics directly from a source and target light distribution is explored. To accompany that, an analytic derivation of theoretical optical efficiency for lens systems is constructed which can improve throughput efficiency by over 150\\%. Then, a complete description of the imaging design problem is presented with some novel algorithmic approaches to solving it including an automated evolutionary sparse optimization algorithm and a machine learning approach. Some description of accelerated raytracing algorithms to speed up these methods will be presented as well.
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653 $a Geometry
653 $a Machine learning
653 $a Optimization
653 $a Raytracing
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690 $a 0800
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