Abstract: Abstract. This article introduces generative uncertainty learning via self-supervised pretraining and transfer learning (GUST), a framework for quantifying free-form geometric uncertainties inherent in the manufacturing ...

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 ...

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 ...

Abstract: Generative artificial intelligence (AI) refers to the domain of AI systems designed to generate new information and artifacts by sampling from complex distributions captured from the data they were trained on. Encompassing ...

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, ...

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, ...