Key, Konstantin

Elgeti, Stefanie

2023-08-10T09:50:55Z

2023-08-10T09:50:55Z

2023-05-31

<div class="csl-bib-body"> <div class="csl-entry">Key, K., &#38; 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> </div>

http://hdl.handle.net/20.500.12708/187754

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

automatic design

deep learning

manufacturing

CAD-based geometric models

physics-informed predictive simulations

Exploration and Exploitation of Deep Learning for Automatic Design

Presentation

Vortrag

Conference Presentation

C6

C3

Modeling and Simulation

Computational System Design

50

50

E317-01 - Forschungsbereich Leichtbau

E317 - Institut für Leichtbau und Struktur-Biomechanik

0009-0008-5263-5776

0000-0002-4474-1666

First International Conference Math 2 Product (M2P 2023)

Deutsche Forschungsgemeinschaft (DFG)

333849990/GRK2379

30-05-2023

01-06-2023

On Site

Event for scientific audience

Taormina

IT

Key, Konstantin

Maschinenbau

2030

100

en

conference paper not in proceedings

none

no Fulltext

Publications

http://purl.org/coar/resource_type/c_18cp

E317-01-1 - Forschungsgruppe Numerische Analyse- und Designmethoden

E317-01 - Forschungsbereich Leichtbau

0009-0008-5263-5776

0000-0002-4474-1666

E317-01 - Forschungsbereich Leichtbau

E317 - Institut für Leichtbau und Struktur-Biomechanik