<div class="csl-bib-body">
<div class="csl-entry">Elgeti, S. (2024, July 25). <i>Splines vs. Neural Networks: How Novel Machine Learning Approaches Influence Design Optimization</i> [Conference Presentation]. 16th World Congress on Computational Mechanics and 4th Pan American Congress on Computational Mechanics, Vancouver, Canada.</div>
</div>
-
dc.identifier.uri
http://hdl.handle.net/20.500.12708/210348
-
dc.description.abstract
Product innovation is a multi-step process: a creative phase where ideas are born, an evaluation phase where the ideas are evaluated, and an implementation phase where these ideas become tangible. While computer-based assistance systems are already available for the latter two phases, creativity is often still considered an exclusively human attribute. However, recent advances in artificial intelligence (AI) have challenged this notion, as creative AI agents are increasingly integrated into our daily lives and have demonstrated their potential to create original content (e.g., ChatGPT, DALL-E, MuseNet, DeepDream). In light of these advances, a new field of research has emerged in the area of AI-enabled design processes, leading to a more-than-human design process in which a computer agent collaborates with a design team to efficiently and creatively explore the entire design space in search of novel design solutions.
To this end, we will demonstrate new technologies, such as how Variational Autoencoders (VAE) can be used to learn low-dimensional, yet feature-rich shape representations. This approach promises significant improvements in both performance and variety of shapes that can be learned. The resulting geometric representation is then incorporated into a shape optimization framework. In addition, we explore the potential of reinforcement learning (RL) as an optimization strategy. RL is based on the trial-and-error interaction of an agent with its environment. As such, RL can be characterized as experience-driven, autonomous learning. While not necessarily superior to classical optimization algorithms (such as gradient-based approaches) for a single optimization problem, based on the existing literature, we expect RL techniques to thrive when recurrent optimization tasks arise.
en
dc.language.iso
en
-
dc.subject
numerical modeling
en
dc.subject
numerical design
en
dc.subject
scientific machine learning
en
dc.title
Splines vs. Neural Networks: How Novel Machine Learning Approaches Influence Design Optimization
en
dc.type
Presentation
en
dc.type
Vortrag
de
dc.type.category
Conference Presentation
-
tuw.publication.invited
invited
-
tuw.researchTopic.id
C6
-
tuw.researchTopic.id
C3
-
tuw.researchTopic.name
Modeling and Simulation
-
tuw.researchTopic.name
Computational System Design
-
tuw.researchTopic.value
50
-
tuw.researchTopic.value
50
-
tuw.publication.orgunit
E317-01-1 - Forschungsgruppe Numerische Analyse- und Designmethoden
-
tuw.author.orcid
0000-0002-4474-1666
-
tuw.event.name
16th World Congress on Computational Mechanics and 4th Pan American Congress on Computational Mechanics
en
tuw.event.startdate
21-07-2024
-
tuw.event.enddate
26-07-2024
-
tuw.event.online
On Site
-
tuw.event.type
Event for scientific audience
-
tuw.event.place
Vancouver
-
tuw.event.country
CA
-
tuw.event.presenter
Elgeti, Stefanie
-
tuw.event.track
Multi Track
-
wb.sciencebranch
Maschinenbau
-
wb.sciencebranch
Informatik
-
wb.sciencebranch
Mathematik
-
wb.sciencebranch.oefos
2030
-
wb.sciencebranch.oefos
1020
-
wb.sciencebranch.oefos
1010
-
wb.sciencebranch.value
60
-
wb.sciencebranch.value
20
-
wb.sciencebranch.value
20
-
item.languageiso639-1
en
-
item.openairetype
conference paper not in proceedings
-
item.grantfulltext
none
-
item.fulltext
no Fulltext
-
item.cerifentitytype
Publications
-
item.openairecristype
http://purl.org/coar/resource_type/c_18cp
-
crisitem.author.dept
E317-01 - Forschungsbereich Leichtbau
-
crisitem.author.orcid
0000-0002-4474-1666
-
crisitem.author.parentorg
E317 - Institut für Leichtbau und Struktur-Biomechanik
