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Clustering Approaches for Faster Nonlinear Projection-Based Model Order Reduction.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Clustering Approaches for Faster Nonlinear Projection-Based Model Order Reduction./
作者:	Anderson, Spenser Lamont.
面頁冊數:	1 online resource (145 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-11, Section: A.
Contained By:	Dissertations Abstracts International84-11A.
標題:	Sparsity. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30398998click for full text (PQDT)
ISBN:	9798379470999

Clustering Approaches for Faster Nonlinear Projection-Based Model Order Reduction.
Anderson, Spenser Lamont.

Clustering Approaches for Faster Nonlinear Projection-Based Model Order Reduction. - 1 online resource (145 pages)

Source: Dissertations Abstracts International, Volume: 84-11, Section: A.

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

Includes bibliographical references

Numerical simulation of large-scale nonlinear dynamical systems, such as those resulting from the discretization of partial differential equations like the Navier-Stokes equation, is increasingly important to science and engineering. Simulating these systems can consume enormous amounts of computational resources, however, especially when many parameterized simulations must be run such as in the context of simulation-based design optimization or model-predictive control. Projection-based model-order reduction is a technology for dramatically reducing the cost of these simulations by approximately solving the governing equations in a data-driven approximation subspace. These methods often produce very substantial cost reductions, but for challenging problems it is still often necessary to obtain even larger speedup factors than are currently possible.This dissertation proposes a set of techniques that exploit the concepts of locality and clustering to accelerate reduced-order models applied to these challenging problems. The first exploits the fact that many physical systems exhibit spatially localized features such as shocks or wakes to construct space-local reduced-order models that are more efficient than existing model-reduction schemes. The second takes advantage of locality in the simulation's state space to construct smaller bases composed of a few nearby solution snapshots. Both novel techniques proposed here are applied to multiple systems drawn from computational fluid dynamics, including a challenging large-scale turbulent flow problem. In all applications, multiple orders of magnitude of speedup are observed relative to the original high-dimensional computational model, and substantial speedups are also observed relative to previously existing model-order reduction techniques.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798379470999Subjects--Topical Terms:

3680690
Sparsity.
Index Terms--Genre/Form:

542853
Electronic books.

Clustering Approaches for Faster Nonlinear Projection-Based Model Order Reduction.
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