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Data-Driven Inverse Design of Metamaterials : = Exploring Complex Physical Fields Beyond Homogenization Assumptions.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Data-Driven Inverse Design of Metamaterials :/
Reminder of title:	Exploring Complex Physical Fields Beyond Homogenization Assumptions.
Author:	Lee, Doksoo.
Description:	1 online resource (287 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-06, Section: B.
Contained By:	Dissertations Abstracts International85-06B.
Subject:	Design. -
Online resource:	click for full text (PQDT)
ISBN:	9798381174731

Data-Driven Inverse Design of Metamaterials : = Exploring Complex Physical Fields Beyond Homogenization Assumptions.
Lee, Doksoo.

Data-Driven Inverse Design of Metamaterials :Exploring Complex Physical Fields Beyond Homogenization Assumptions. - 1 online resource (287 pages)

Source: Dissertations Abstracts International, Volume: 85-06, Section: B.

Thesis (Ph.D.)--Northwestern University, 2023.

Includes bibliographical references

Metamaterials are engineered material systems that support superior, often exotic, functionalities due to structure rather than composition. Since their advent, design of metamaterials has been a pressing quest that will in part embody the sought-after ideal of the multiscale architectures. Due to the hierarchical nature and emergent behaviors, the design presents grand challenges, most of which are far beyond the scope of conventional design paradigms. Succeeding them, data-driven metamaterials design (DMD) has recently invigorated a paradigm shift of metamaterials design. Despite the recent proliferation, we disclose that the primary scope has largely dwelt on the "comfort zone" of simple benchmarks in metamaterials design, many of which can be tackled via marrying off-the-shelf implementations of machine learning algorithms and homogenization assumptions. Accommodating the untapped complexities beyond those assumptions is, arguably, key to bringing metamaterials closer to their fruition. Seeking to move forward to the direction, this dissertation is dedicated to building DMD frameworks for systems whose behavior involves complex physics beyond homogenization assumptions, with special attention to photonic metasurfaces. Aiming to deliver a suite of principled design method and insightful findings associated with central research questions, we specify research tasks at the module level, each of which closely aligns with core components of DMD. Synthesis of the module-level strides achieved in this dissertation streamlines the otherwise overwhelming inverse designs involving complex physical fields beyond homogenization assumptions, allows wide applicability by virtue of high modularity, and could propel data mining and knowledge management for generic DMD purposes.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798381174731Subjects--Topical Terms:

595500
Design.
Subjects--Index Terms:

MetamaterialsIndex Terms--Genre/Form:

554714
Electronic books.

Data-Driven Inverse Design of Metamaterials : = Exploring Complex Physical Fields Beyond Homogenization Assumptions.
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