Projects per year
Methods for learning and compressing high-dimensional data allow designers to generate novel and low-dimensional design representations for shape optimization problems. By using compact design spaces, global optimization algorithms require less function evaluations to characterize the problem landscape. Furthermore, data-driven representations are often domain-agnostic and independent of the user expertise, and thus potentially capture more relevant design features than a human designer would suggest. However, more factors than the dimensionality play a role in the efficiency of design representations. In this paper, we perform a comparative analysis of design representations for 3D shape optimization problems obtained with principal component analysis, kernel-principal component analysis and a 3D point cloud autoencoder, which we apply on a benchmark data set of computer aided engineering car models. We evaluate the shape-generative capabilities of these methods and show that we can modify the geometries more locally with the autoencoder than with the remaining methods. In a vehicle aerodynamic optimization framework, we verify that this property of the autoencoder representation improves the optimization performance by enabling potentially complementary degrees of freedom for the optimizer. With our study, we provide insights on the qualitative properties and quantifiable measures on the efficiency of deep neural networks as shape generative models for engineering optimization problems, as well as analyses of geometric representations for engineering optimization with evolutionary algorithms.
|Title of host publication||2021 IEEE Congress on Evolutionary Computation (CEC)|
|Number of pages||8|
|ISBN (Electronic)||9781728183930, 9781728183923 (USB)|
|ISBN (Print)||9781728183947 (PoD)|
|Publication status||Published - 9 Aug 2021|
|Event||2021 IEEE Congress on Evolutionary Computation (CEC) - Kraków, Poland|
Duration: 28 Jun 2021 → 1 Jul 2021
|Conference||2021 IEEE Congress on Evolutionary Computation (CEC)|
|Period||28/06/21 → 1/07/21|
Bibliographical noteThis project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement number 766186 (ECOLE).
- Analytical models
- Three-dimensional displays
- Evolutionary computation
- Benchmark testing
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- 1 Finished
European Commission - Management Costs, European Commission
1/04/18 → 31/03/22