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GAN-DUF: Hierarchical Deep Generative Models for Design Under Free-Form Geometric Uncertainty
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Deep generative models have demonstrated effectiveness in learning compact and expressive design representations that significantly improve geometric design optimization. However, these models do not consider the uncertainty ...
t-METASET: Task-Aware Acquisition of Metamaterial Datasets Through Diversity-Based Active Learning
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Inspired by the recent achievements of machine learning in diverse domains, data-driven metamaterials design has emerged as a compelling paradigm that can unlock the potential of multiscale architectures. The model-centric ...
The Future of Digital Twin Research and Development
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: While digital twin (DT) has made significant strides in recent years, much work remains to be done in the research community and in the industry to fully realize the benefits of DT. A group of 25 industry professionals, ...