<div class="csl-bib-body">
<div class="csl-entry">Key, K., & Elgeti, S. (2023, May 31). <i>Exploration and Exploitation of Deep Learning for Automatic Design</i> [Conference Presentation]. First International Conference Math 2 Product (M2P 2023), Taormina, Italy. http://hdl.handle.net/20.500.12708/187754</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/187754
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dc.description.abstract
Computer-aided engineering (CAE) allows to analyze and optimize both products and processes and furthermore provides insight into their reliability. CAE can, for example, facilitate the automatic optimization of engineering designs. Design optimization problems are constituted by design objectives, physical constraints, and geometrical representations. The given geometries together with the provided physics can then form the basis for predictive simulations — whose results can be further exploited to automatically optimize designs with respect to the defined objectives.
In the past, many different approaches towards CAE in general and design optimization in particular have been explored. In terms of predictive simulations, apart from application-specific methods, an important field is the generation of reduced-order models in order to accommodate the many-query scenario of automatic design optimization. In the context of geometrical representations, these include vertex-based and spline-based methods. Despite the past successes, the value of these approaches can be further enhanced based on the advent and maturing of machine learning techniques such as deep learning [1]. Deep learning is attracting an increasing amount of attention in all fields of CAE and has, in fact, some interesting capabilities: it can seamlessly combine models and data and allows to identify lower-dimensional parameterizations. Consequently, all three above-mentioned constituents of automatic design tasks can benefit from deep learning.
We will investigate the benefits of deep learning in automatic design problems that originate from the field of manufacturing. Possible examples are deep neural networks in the context of CAD-based modeling of geometries or physics-informed predictions of solidification in manufacturing processes [2].
[1] I. Goodfellow, Y. Bengio, A. Courville, Deep Learning. MIT Press (2016)
[2] K. Key, Novel Numerical Methods for Solidification in Manufacturing Processes. Dissertation (2022)
en
dc.language.iso
en
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dc.subject
automatic design
en
dc.subject
deep learning
en
dc.subject
manufacturing
en
dc.subject
CAD-based geometric models
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dc.subject
physics-informed predictive simulations
en
dc.title
Exploration and Exploitation of Deep Learning for Automatic Design
en
dc.type
Presentation
en
dc.type
Vortrag
de
dc.type.category
Conference Presentation
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tuw.researchTopic.id
C6
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tuw.researchTopic.id
C3
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tuw.researchTopic.name
Modeling and Simulation
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tuw.researchTopic.name
Computational System Design
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tuw.researchTopic.value
50
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tuw.researchTopic.value
50
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tuw.publication.orgunit
E317-01 - Forschungsbereich Leichtbau
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tuw.publication.orgunit
E317 - Institut für Leichtbau und Struktur-Biomechanik
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tuw.author.orcid
0009-0008-5263-5776
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tuw.author.orcid
0000-0002-4474-1666
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tuw.event.name
First International Conference Math 2 Product (M2P 2023)
en
dc.description.sponsorshipexternal
Deutsche Forschungsgemeinschaft (DFG)
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dc.relation.grantnoexternal
333849990/GRK2379
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tuw.event.startdate
30-05-2023
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tuw.event.enddate
01-06-2023
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tuw.event.online
On Site
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tuw.event.type
Event for scientific audience
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tuw.event.place
Taormina
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tuw.event.country
IT
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tuw.event.presenter
Key, Konstantin
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wb.sciencebranch
Maschinenbau
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wb.sciencebranch.oefos
2030
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wb.sciencebranch.value
100
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item.cerifentitytype
Publications
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item.cerifentitytype
Publications
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item.fulltext
no Fulltext
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item.languageiso639-1
en
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item.grantfulltext
none
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item.openairecristype
http://purl.org/coar/resource_type/c_18cf
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item.openairecristype
http://purl.org/coar/resource_type/c_18cf
-
item.openairetype
Presentation
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item.openairetype
Vortrag
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crisitem.author.dept
E317-01 - Forschungsbereich Leichtbau
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crisitem.author.dept
E317-01 - Forschungsbereich Leichtbau
-
crisitem.author.orcid
0009-0008-5263-5776
-
crisitem.author.orcid
0000-0002-4474-1666
-
crisitem.author.parentorg
E317 - Institut für Leichtbau und Struktur-Biomechanik
-
crisitem.author.parentorg
E317 - Institut für Leichtbau und Struktur-Biomechanik