Generative adversarial networks for increasing the veracity of big data

Dering, Matthew L.; Tucker, Conrad S.

doi:10.1109/bigdata.2017.8258219

Cited by 15 publications

(7 citation statements)

References 17 publications

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“…Two design agents were constructed on top of Energy3D to scaffold divergent and convergent design processes, respectively, both of which were powered by genetic algorithms (Schimpf et al, 2018). Furthermore, generative design agents have been developed to produce design alternatives utilizing Generative Adversarial Networks (GANs) (Dering and Tucker, 2017), Recurrent Neural Networks (RNNs) (Stump et al, 2019), and convolutional networks (Dosovitskiy et al, 2017;Raina et al, 2019). A generative design module has also been included in Siemens NX (Haubrock and Bevan, 2017).…”

Section: Developing Intelligent Agentsmentioning

confidence: 99%

Toward Hybrid Teams: A Platform to Understand Human-Computer Collaboration During the Design of Complex Engineered Systems

Song

Zurita

Zhang

et al. 2020

Proc. Des. Soc.: Des. Conf.

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Human-computer hybrid teams can meet challenges in designing complex engineered systems. However, the understanding of interaction in the hybrid teams is lacking. We review the literature and identify four key attributes to construct design research platforms that support multi-phase design, hybrid teams, multiple design scenarios, and data logging. Then, we introduce a platform for unmanned aerial vehicle (UAV) design embodying these attributes. With the platform, experiments can be conducted to study how designers and intelligent computational agents interact, support, and impact each other.

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Section: Developing Intelligent Agentsmentioning

confidence: 99%

Toward Hybrid Teams: A Platform to Understand Human-Computer Collaboration During the Design of Complex Engineered Systems

Song

Zurita

Zhang

et al. 2020

Proc. Des. Soc.: Des. Conf.

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“…Data-driven design synthesis, which is increasingly becoming more popular [15], learns rules from a database and generates plausible new designs with similar structure and function to existing ones. Dimensionality reduction techniques which allow inverse transformations from the latent space back to the design space are a commonly used data-driven design synthesis method.…”

Section: Design Synthesismentioning

confidence: 99%

PaDGAN: A Generative Adversarial Network for Performance Augmented Diverse Designs

Chen¹,

Ahmed²

2020

Preprint

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Deep generative models are proven to be a useful tool for automatic design synthesis and design space exploration. When applied in engineering design, existing generative models face two challenges: 1) generated designs lack diversity and do not cover all areas of the design space and 2) it is difficult to explicitly improve the overall performance or quality of generated designs without excluding low-quality designs from the dataset, which may impair the performance of the trained model due to reduced training sample size. In this paper, we simultaneously address these challenges by proposing a new Determinantal Point Processes based loss function for probabilistic modeling of diversity and quality. With this new loss function, we develop a variant of the Generative Adversarial Network, named "Performance Augmented Diverse Generative Adversarial Network" or PaDGAN, which can generate novel high-quality designs with good coverage of the design space. We demonstrate that PaDGAN can generate diverse and high-quality designs on both synthetic and real-world examples and compare PaDGAN against other models such as the vanilla GAN and the BézierGAN. Unlike typical generative models that usually generate new designs by interpolating within the boundary of training data, we show that PaDGAN expands the design space boundary towards high-quality regions. The proposed method is broadly applicable to many tasks including design space exploration, design optimization, and creative solution recommendation.

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“…The process of generating new content to use as a training dataset can require significant time and resources [5][6][7]. In recent years, researchers have started exploring how realistic, synthetic data can be automatically generated [31,32]. However, while studies have shown that these approaches can generate synthetic datasets that cannot be accurately distinguished from human-generated ones [33][34][35], they still require some initial datasets to train their models.…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning for Procedural Content Generation of 3D Virtual Environments

López

Cunningham

Ashour

et al. 2020

Journal of Computing and Information Science in Engineering

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This work presents a deep reinforcement learning (DRL) approach for procedural content generation (PCG) to automatically generate three-dimensional (3D) virtual environments that users can interact with. The primary objective of PCG methods is to algorithmically generate new content in order to improve user experience. Researchers have started exploring the use of machine learning (ML) methods to generate content. However, these approaches frequently implement supervised ML algorithms that require initial datasets to train their generative models. In contrast, RL algorithms do not require training data to be collected a priori since they take advantage of simulation to train their models. Considering the advantages of RL algorithms, this work presents a method that generates new 3D virtual environments by training an RL agent using a 3D simulation platform. This work extends the authors’ previous work and presents the results of a case study that supports the capability of the proposed method to generate new 3D virtual environments. The ability to automatically generate new content has the potential to maintain users’ engagement in a wide variety of applications such as virtual reality applications for education and training, and engineering conceptual design.

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Generative adversarial networks for increasing the veracity of big data

Cited by 15 publications

References 17 publications

Toward Hybrid Teams: A Platform to Understand Human-Computer Collaboration During the Design of Complex Engineered Systems

Toward Hybrid Teams: A Platform to Understand Human-Computer Collaboration During the Design of Complex Engineered Systems

PaDGAN: A Generative Adversarial Network for Performance Augmented Diverse Designs

Deep Reinforcement Learning for Procedural Content Generation of 3D Virtual Environments

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