1,252 research outputs found
UMSL Bulletin 2023-2024
The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp
UMSL Bulletin 2022-2023
The 2022-2023 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1087/thumbnail.jp
"Le present est plein de l’avenir, et chargé du passé" : Vorträge des XI. Internationalen Leibniz-Kongresses, 31. Juli – 4. August 2023, Leibniz Universität Hannover, Deutschland. Band 3
[No abstract available]Deutschen Forschungsgemeinschaft (DFG)/Projektnr. 517991912VGH VersicherungNiedersächsisches Ministerium für Wissenschaft und Kultur (MWK
Learning-based generative representations for automotive design optimization
In automotive design optimizations, engineers intuitively look for suitable representations of CAE models that can be used across different optimization problems. Determining a suitable compact representation of 3D CAE models facilitates faster search and optimization of 3D designs. Therefore, to support novice designers in the automotive design process, we envision a cooperative design system (CDS) which learns the experience embedded in past optimization data and is able to provide assistance to the designer while performing an engineering design optimization task. The research in this thesis addresses different aspects that can be combined to form a CDS framework.
First, based on the survey of deep learning techniques, a point cloud variational autoencoder (PC-VAE) is adapted from the literature, extended and evaluated as a shape generative model in design optimizations. The performance of the PC-VAE is verified with respect to state-of-the-art architectures. The PC-VAE is capable of generating a continuous low-dimensional search space for 3D designs, which further supports the generation of novel realistic 3D designs through interpolation and sampling in the latent space. In general, while designing a 3D car design, engineers need to consider multiple structural or functional performance criteria of a 3D design. Hence, in the second step, the latent representations of the PC-VAE are evaluated for generating novel designs satisfying multiple criteria and user preferences. A seeding method is proposed to provide a warm start to the optimization process and improve convergence time. Further, to replace expensive simulations for performance estimation in an optimization task, surrogate models are trained to map each latent representation of an input 3D design to their respective geometric and functional performance measures. However, the performance of the PC-VAE is less consistent due to additional regularization of the latent space.
Thirdly, to better understand which distinct region of the input 3D design is learned by a particular latent variable of the PC-VAE, a new deep generative model is proposed (Split-AE), which is an extension of the existing autoencoder architecture. The Split-AE learns input 3D point cloud representations and generates two sets of latent variables for each 3D design. The first set of latent variables, referred to as content, which helps to represent an overall underlying structure of the 3D shape to discriminate across other semantic shape categories. The second set of latent variables refers to the style, which represents the unique shape part of the input 3D shape and this allows grouping of shapes into shape classes. The reconstruction and latent variables disentanglement properties of the Split-AE are compared with other state-of-the-art architectures. In a series of experiments, it is shown that for given input shapes, the Split-AE is capable of generating the content and style variables which gives the flexibility to transfer and combine style features between different shapes. Thus, the Split-AE is able to disentangle features with minimum supervision and helps in generating novel shapes that are modified versions of the existing designs.
Lastly, to demonstrate the application of our initial envisioned CDS, two interactive systems were developed to assist designers in exploring design ideas. In the first CDS framework, the latent variables of the PC-VAE are integrated with a graphical user interface. This framework enables the designer to explore designs taking into account the data-driven knowledge and different performance measures of 3D designs. The second interactive system aims to guide the designers to achieve their design targets, for which past human experiences of performing 3D design modifications are captured and learned using a machine learning model. The trained model is then used to guide the (novice) engineers and designers by predicting the next step of design modification based on the current applied changes
Split Federated Learning for 6G Enabled-Networks: Requirements, Challenges and Future Directions
Sixth-generation (6G) networks anticipate intelligently supporting a wide
range of smart services and innovative applications. Such a context urges a
heavy usage of Machine Learning (ML) techniques, particularly Deep Learning
(DL), to foster innovation and ease the deployment of intelligent network
functions/operations, which are able to fulfill the various requirements of the
envisioned 6G services. Specifically, collaborative ML/DL consists of deploying
a set of distributed agents that collaboratively train learning models without
sharing their data, thus improving data privacy and reducing the
time/communication overhead. This work provides a comprehensive study on how
collaborative learning can be effectively deployed over 6G wireless networks.
In particular, our study focuses on Split Federated Learning (SFL), a technique
recently emerged promising better performance compared with existing
collaborative learning approaches. We first provide an overview of three
emerging collaborative learning paradigms, including federated learning, split
learning, and split federated learning, as well as of 6G networks along with
their main vision and timeline of key developments. We then highlight the need
for split federated learning towards the upcoming 6G networks in every aspect,
including 6G technologies (e.g., intelligent physical layer, intelligent edge
computing, zero-touch network management, intelligent resource management) and
6G use cases (e.g., smart grid 2.0, Industry 5.0, connected and autonomous
systems). Furthermore, we review existing datasets along with frameworks that
can help in implementing SFL for 6G networks. We finally identify key technical
challenges, open issues, and future research directions related to SFL-enabled
6G networks
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